KANYOK, NATHAN J., M.S., December 2019 COMPUTER SCIENCE

SITUATIONAL AWARENESS MONITORING FOR HUMANS-IN-THE-LOOP OF TELEPRES-

ENCE ROBOTIC SYSTEMS (80 pages)

Thesis Advisor: Dr. Jong-Hoon Kim

Autonomous automobiles are expected to be introduced in increasingly complex increments until the system is able to navigate without human interaction. However, humanity is uncomfortable with relying on algorithms to make security critical decisions, which often have moral dimensions.

Many robotic systems keep humans in the decision making loop due to their unsurpassed ability to perceive contextual information in ways we find relevant.

It is likely that we will see transportation systems with no direct human supervision necessary, but these systems do not address our worry about moral decisions. Until we are able to embed moral agency in digital systems, human actors will be the only agents capable of making decisions with security-critical and moral components.

Additionally, in order for a human to be in the position that we can have confidence in their decision, they must be situationally aware of the environment in which the decision will be made.

Virtual reality as a medium can achieve this by allowing a person to be telepresent elsewhere.

A telepresence dispatch system for autonomous transportation vehicles is proposed that places emphasis on situational awareness so that humans can properly be in the decision making loop.

Pre-trial, in-trial, and post-trial metrics are gathered that emphasize human health and monitor situational awareness through traditional and novel approaches. SITUATIONAL AWARENESS MONITORING FOR HUMANS-IN-THE-LOOP OF

TELEPRESENCE ROBOTIC SYSTEMS

A thesis submitted

to Kent State University

in partial fulfillment of the requirements

for the degree of Master of Science

by

Nathan J. Kanyok

December 2019

© Copyright All rights reserved

Except for previously published materials Thesis written by

Nathan J. Kanyok

B.S., Bowling Green State University, 2016

M.S., Kent State University, 2019

Approved by

Dr. Jong-Hoon Kim , Advisor

Dr. Javed I. Kahn , Chair, Department of Computer Science

James L. Blank , Dean, College of Arts and Sciences TABLE OF CONTENTS

TABLE OF CONTENTS ...... iv

LIST OF FIGURES ...... viii

LIST OF TABLES ...... ix

ACKNOWLEDGMENTS ...... x

1 Introduction ...... 1

1.1 Overview ...... 1

2 Literature Review ...... 4

2.1 Introduction to Literature Review ...... 4

2.2 Robotics ...... 5

2.2.1 Formalization and History ...... 5

2.2.2 General Features of Robots ...... 7

2.2.3 Sense, Think, Act Cycle ...... 8

2.2.4 Autonomy and Control ...... 9

2.2.5 Self-Driving Car ...... 10

2.2.6 Multidisciplinary ...... 10

2.2.7 Robotic Systems: ...... 13

2.2.8 Public Perception of Robotics ...... 15

2.2.9 Robot Ethics ...... 16

2.3 Telepresence ...... 16

2.3.1 History of Telepresence ...... 16

2.3.2 Psychology of Telepresence ...... 18

2.3.3 Telepresence and Teleoperation ...... 18

2.3.4 Modalities ...... 19

iv 2.3.5 Telepresence Design ...... 19

2.3.6 Applications of Telepresence ...... 20

2.3.7 Drawbacks of Telepresence ...... 21

2.4 Virtual Reality ...... 21

2.4.1 History of Virtual Reality ...... 21

2.4.2 Applications of Virtual Reality ...... 22

2.4.3 Types of Virtual Reality ...... 24

2.4.4 Drawbacks of Virtual Reality ...... 25

2.4.5 Measuring Virtual Reality ...... 27

2.5 Human System Interactions ...... 28

2.5.1 Human Machine Interaction ...... 29

2.5.2 Human Computer Interaction ...... 29

2.5.3 Human Robot Interaction ...... 31

2.6 Human Factors ...... 35

2.6.1 Definition of Human Factors ...... 35

2.6.2 Role of Human Psychology ...... 36

2.6.3 Human Factors Summary ...... 37

2.7 Human-In-the-Loop ...... 38

2.7.1 Definition of Human-in-the-Loop Systems ...... 38

2.7.2 Security Critical Functions ...... 39

2.7.3 Performance Factors ...... 39

2.7.4 Information Processing ...... 40

2.8 Philosophy ...... 41

2.8.1 Plato’s Allegory of the Cave ...... 41

2.8.2 Leibniz’s Possible Worlds ...... 42

2.8.3 Descartes’ Dualism ...... 43

2.8.4 Ethical Theory ...... 44

2.9 Literature Conclusion ...... 45

v 3 Proposed System ...... 46

3.1 Proposed System ...... 46

3.1.1 Motivation ...... 46

3.1.2 Architecture ...... 47

3.1.3 Trust Within the System ...... 49

3.2 Assumptions ...... 51

4 Materials and Methods ...... 53

4.1 Materials ...... 53

4.1.1 Robot Model ...... 53

4.1.2 Software ...... 55

4.1.3 Hardware ...... 56

4.1.4 Experiment Setup ...... 57

5 Results ...... 63

5.0.1 Demographics ...... 63

5.0.2 Pre-Trial Health Survey ...... 63

5.0.3 In-Trial ...... 63

5.0.4 Post-Trial ...... 64

6 Discussion ...... 67

6.1 Discussion of Results ...... 67

6.1.1 Pre-Trial ...... 67

6.1.2 Health ...... 67

6.1.3 In-Trial ...... 67

6.1.4 Post-Trial ...... 69

6.2 Application of Results ...... 69

6.3 Going Forward ...... 70

6.4 Future Work ...... 71

6.5 Obstacles of Implementation ...... 72

6.6 Summary ...... 74

vi 7 Conclusion ...... 75

7.1 Literature Review ...... 75

7.2 Design of Human-in-the-Loop Telepresent Dispatch System for Autonomous Robots . 75

7.3 Experiment ...... 76

7.4 Future Work ...... 76

7.5 Summary ...... 76

BIBLIOGRAPHY ...... 77

vii LIST OF FIGURES

1 Depiction of the Trolley Problem [1] ...... 2

2 Leonaro da Vinci’s ’Mechanical’ Creations ...... 5

3 Unimate ...... 7

4 Sense-Think-Act Cycle ...... 8

5 Academic Disciplines of Robotics ...... 11

6 Sutherland’s Sword of Damocles ...... 22

7 Areas of HRI ...... 32

8 rViz Environment ...... 55

9 Gazebo Simulation of Willow Garage ...... 55

10 Multiple Robot Monitor View ...... 57

11 Multiple Robot Virtual Reality View ...... 57

12 Map of Simulation Environment ...... 59

viii LIST OF TABLES

1 Symptoms Checklist for Health Survey ...... 60

2 Results of In-Trials ...... 64

3 Results of Post-Trial Likert Questions ...... 65

ix ACKNOWLEDGMENTS

I would like to first acknowledge my committee members, Dr. Gokarna Sharma and Dr. Austin

Melton, as well as my advisor Dr. Jong-Hoon Kim. They have provided me with great guidance and feedback. Next, I would like to thank the members of the ATR-Lab for the support and encouragement, specifically Xiangxu Lin and Alfred Shaker for their development roles. Finally, I would like to thank my friends and family for supporting me along the way.

x CHAPTER 1

Introduction

1.1 Overview

Confidence in decision making stems from Situational Awareness (SA), which serves as a measure to determine the quality of human judgments. If the operator does not posses a high degree of situational awareness, why would a system opt to include them in the decision making loop? SA indicates if the human-in-the-loop is in a position to provide insight to a security critical decision. A security critical decision involves a degree of harm, either immediate or future, to an object of value.

Often times, especially in the case of an autonomous automobile, a minor miscue, misinterpretation, or misdirection can have a catastrophic result. In the best case, minor environmental damage is done, perhaps a broken tree or bent bumper. The worst case is exponentially more dire. If sufficient prediction and planning were possible, there would be no need for human intervention.

Alternatively, out current metaphysics accepts human decision making, under normal circum- stances, to be a baseline for how we judge and interpret results. When someone reacts, we expect them to do so to the best ability that their mental facilities and physical attributes allow. An average human is responsible only in so far as they acted in a sufficient manner. The ability to do otherwise, and their knowledge of the circumstances, are the primary features in judging blameworthiness of actions. Normative ethical theories provide a formalized way of interpreting the morality of a said action. When a driver swerves into another vehicle to avoid hitting a child that has ran onto the roadway, the driver’s action is accepted in so far as them not doing so would have produced (or likely produced) less favorable set of events. This is not always so clear, as illustrated in the trolley problem, which poses the question of what action would one do when both options are not desirable

[1]. See Figure 1 for a visualization of the trolley problem. Each normative theory addresses this ethical dilemma differently, leaving us in a stalemate as to what the best option is.

To embed the facilities to reason the way an average human would into an artificial intelligence

1 has been long identified as a difficult task, one which we have made very little progress in. Simply programming rules of morality is not enough to handle the dynamic way our world is perceived to work.

Until we have discovered a way to create arti-

ficial moral agency, we will be uncomfortable

with algorithms being the sole influences on se-

curity critical decision making. Thus, the case

for having a human in the loop is clear: rea-

soning about security critical decisions requires

moral facilities not yet achievable.

In controlled isolation, our best planning

Figure 1: Depiction of the Trolley Problem [1] algorithms act sufficiently, in ways we expect them to. But, when deployed “in the wild” they frequently preform far below natural human cognition. The best methods for incorporating humans in the loop are not yet generalized. We know methods that fail, but the optimal methods are yet to be established. Each application has its own intricacies, but principles of design do exist. As systems are developed, the information presentation to humans and their overall role within the system will need to be reexamined. However, existing work affords us a direction to head in, on the shoulders of our fathers.

It is the case that virtual reality is an effective medium to achieve telepresence. If a human cannot physically be in a position to act, the next best thing is for them to be telepresent at that location. Telepresence requires a person to shift their situational awareness from their local selves to a remote environment. This commonly takes place in the form of a digital avatar.

Humans, even with the help of technology, are folly to mistakes, but are the only acceptable agents to make such decisions. To be able to properly make security critical decisions, they have to be aware of relevant factors in such a way to justify their actions. Telepresence allows for humans to justifiably make decisions about remote environments.

This thesis presents the design of virtual reality mediated telepresence dispatch system for autonomous transportation vehicles. The proposed system emphasizes situational awareness as the key component that dictates confidences of an operator’s action. In order to test that virtual reality

2 mediated telepresence is more effective than traditional methods of telepresence, a novel situational awareness metric is introduced, Continuous Situational Awareness Monitoring (CSAM).

3 CHAPTER 2

Literature Review

2.1 Introduction to Literature Review

A telepresence dispatch system is required to be built on a multidisciplinary foundation of research.

To not consider related fields and the work produced by them would prompt designers to repeat past mistakes. However novel a proposed system may be, several different areas of research are directly relevant for establishing its validity. Ignorance of this work would likely result in repetitive and uninspired developments. Work in robotics, telepresence, virtual reality, Human-System Interaction, and Human Factors all are essential, with philosophical theories tying together their cogency.

• Robotics provides the foundation for how such a system could be constructed.

• Telepresence allows for a human to place their situational awareness elsewhere.

• Virtual Reality is an effective medium to achieve telepresence.

• Human-in-the-Loop bring humans into the decision making process.

• Human-Systems Interactions serves as a benchmark for understanding how humans co- operate with advanced systems.

• Human Factors establishes design principles for how humans interact with systems.

• Philosophy supports and rejects different approaches.

These yet emerging technologies have an existing body of literature to critically discuss the impact and conditions of telepresence, robotics, and virtual reality. The following sections will take a historical approach to understanding how these fields relate to each other, with the overall narrative focused on the proposed design for a future telepresence robot dispatch system.

4 2.2 Robotics

A general definition of robotics is quite difficult to establish without understanding the intricacies that make up the field. Like finding a general definition for a “game,” it serves no purpose trying to create an exhaustive list of specific features each one shares. There are clear examples of things that are robots and things that are not, the distinction between the two is blurry. However, abstract principles can be found which provide a baseline for establishing whether or not a system is robotic.

2.2.1 Formalization and History

Origin of Name

Robots are now ubiquitous with technological progress. The field of robotics needs little introduc- tion to the human psyche, with frequent references in popular culture.

In fact, the concept of mechanical agents is not novel to recent in- dustrialized humans. Homer’s Illiad introduces “golden servants” that are cognisant agents in the world, in 1190 BCE. Leonardo da

Vinci drafted a “mechanical” knight and lion in 1495, see Figure

2.

Far before humans had the cotton gin , their creativity fore- saw embodying physical constructions with decision making abil- ities, what we would now call a “robot”. The usefulness of these creations was not lost on their creators, with high hopes of the potential applications of robotics constantly propelling the field.

The world “robot” was first used in its modern parliaments by Figure 2: Leonaro da Vinci’s

Capek in 1921, but the concept has existed for several millennia ’Mechanical’ Creations before. Robots, now widely implemented, serve to fill both new and old societal roles.

Before robotic systems were successfully created, they already had an informal rule of gover- nance. The influential science-fiction author Isaac Asimov wrote the first necessary commandments of robot systems in a series of short stories published between 1938 and 1942 [21]. Asimov’s Rules

5 of Robotics capture the general concern with embodying consciousness in a machine and its impact on humanity. The rules are formalized as the following:

1) A robot may not injure a human being or through inaction allow a human to come

to harm

2) A robot must obey orders given it by humans except when doing so conflicts with

the first law

3) A robot must protect its own existence as long as this does not conflict with the first

or second law

Asimov later added the 0th rule was later appended 1985, being

0) A robot may not injure humanity, or, through inaction, allow humanity to come to

harm

The 0th rule expands the duty of the robot to humanity as a whole and not to a single individual.

These rules focus on dealing with effects of having autonomous machines, with something akin to free will.

Even though Asimov’s rules have been shown to be less than all encompassing, they are con- sidered to be a foundation for what a code of robot behavior must be. Applying rule-based logic to robotics is an intuitive way to begin orchestrating their behavior, but it is not fool-proof. Asimov’s laws are more so a guideline than a way of programming a system [6].

Early Systems

Using machines with embodied knowledge, such as Da Vinci’s knight, has always been seen a solution to several problems. The culture surrounding robotics laid the groundwork for what was to be expected as these systems were to come to fruition. The robot would primarily serve some service deemed necessary or useful by their human counterparts. Often times, this relation is described as master-slave. The earliest robotic systems solved immediate problems facing the manufacturing industry.

Largely seen as the first deployed robot, the Unimate was an automobile production assistant

[21]. General Motors introduced Unimate in 1961 [43], see Figure 3.

6 Robot assistance has also been the goal of the medical field, with

the first surgical robot being utilized in 1985 [22]. DARPA was the ini-

tial supporter, looking to create battlefield surgeons. Several incremental

improvements to effectiveness and usability have followed, with telepres-

Figure 3: Unimate ence being highlighted as a significant contributor to performance. Each deployment of a new robot inspires more to come.

2.2.2 General Features of Robots

The necessary and sufficient conditions for what constitutes a robot are best defined loosely. For too fine-grained of a definition limits conceptualization. However, for any one component is missing, we can be sure to not categorize it as robot.

While the actual deployment and use of robots largely varies, all robotic systems share a common set of components and features.

Every robot will have, in some form or another, the following:

• Actuators

• Computing Unit

• Sensor Suite

• Physical Embodiment

A robot must use these components to engage in the sense-think-act cycle.

Actuators: Actuators allow for the robot to express behavior and interact physically with the environment. Wheels, arms, grippers, speakers, and the like are all included as being actuators.

Essentially, they function as the output onto the world as a result of being given a command.

Computing Unit: In order for decisions to be made and the corresponding actions be enacted, some sort of processing is required to transfer data into knowledge for decision making. Utilizing information, a computing unit coordinates an response, functioning largely like the brain of a human. Sensory data is fed, and appropriate outcomes are delegated to the actuators of the system.

Not being able to rely on a deterministic environment, like software systems, robotics requires a method of dealing with degrees of uncertainty of both information and the condition of the world.

7 Sensor Suite: The environment, both internal and external to the robot, is perceived by the robot’s sensor suite. Much like how the five senses of humans have their corresponding perceptual systems, a robot is embodied with a way to capture data about the world and itself. This raw data is received by a sensor and then produces information about the robot and is relation to the world. External sensors gather data about the environment in which a robot is situated. On the other hand, internal sensors gather data about the robot itself, such as position of actuators and temperature of vital components. Through the information produced by the sensor suite, a robot is later able to form knowledge representations and make well-informed decisions.

Physical Embodiment: The robot, being more than just software, requires a physical embod- iment of itself. Simply determining signals to be sent to actuators by using sensor information is not enough. The body of robot is largely determined by the tasks it will be executing and the environment it will be operating in. The look of a social service robot for elderly care can be very different that the design of a robot meant to clean holding tanks for chemicals.

2.2.3 Sense, Think, Act Cycle

In addition to the general features all robots share, their behavior is largely seen as sharing a similar cycle. A robotic system is often characterized by have the ability to sense, think, and act, see Figure

4. Seeing involves sensing the environment. This is achieved through the coordination of sensors.

Thinking is the decision making aspect of a robotic system.

Utilizing information gathered from sensors, a robot must plan

what the next appropriate actions are, given its goal and the

physical limitations of the robot.

The acting part of the cycle is execution of some action as

the result of the plan provided during the thinking portion.

A robot performs these three functionalities ad infinitum.

Only when a task is deemed complete or some unforeseen cir- Figure 4: Sense-Think-Act Cycle cumstance occurs this cycle is broken.

This cycle is similar to the three stages of human information processing as seen in Section

2.6.2.

Each of the functionalities may vary in degree of complexity and efficiency due to the exact

8 implementation and design of a the robot.

2.2.4 Autonomy and Control

Robotic systems, diverse in design, illustrate several different modes and methods of control. Where a robot lays on the scale of autonomy often dictates the method of control. As autonomy increase, the level of human intervention decreases. Robot control is often thought of as sliding continuum ranging from teleoperation, to full scale autonomy. It is important to note how the degree of human- in-the-loop changes as result of the robots placement on this scale.

Humans can be directly responsible for a large degree of a robot’s functionality. A teleoperated robot relies on human input for movement, manipulation, and cognition. In many ways, most of the sense-think-act cycle is handled by humans when a robot is teleoperated. Small actions can be handled by the robot, but the human is responsible for navigation, dexterity, and manipulation tasks. The teleoperated robot is seen as a dependent extension of the human operator.

Semi-autonomous robots have a variable amount of both human control and system control.

One or more functionalities are performed by a robot. For example, cognitive decision making can be handled by the human operator, where localization and image detection is performed by autonomous functions of the system. A large portion of robots fall into this category, with some functions being autonomous while also relying on humans for input. Fully autonomous robots, then, handle the entire sense-think-act cycle on their own. The robot can act on its own, with no supervision or interaction from humans. The effectiveness of operation is constrained by the design of the system.

Autonomy versus Automation: Automated vehicles, as presented, require a human fallback.

Automation is considered to be largely deterministic when compared to autonomy [17]. The engine

firing rate and synchronization is an example of an automated process. Whereas autonomy pertains to how well a system acts without supervision or control. Environments, being dynamic, do not provide the deterministic considerations that an automated process requires. When higher-level decision making and real-time reaction takes place, we are in the domain of autonomy.

In general, when we speak of self-driving cars we are dealing with automated auto-mobility. The comparison to automated aviation serves to describe some similarities, however, aviation systems deal with much less relevant information when compared to ground transportation [17]. The in-

9 creased amount of unpredictable actors causes constraints on how much confidence can be afforded to the planning of the system.

2.2.5 Self-Driving Car

Transportation of people, goods, and services encompasses large portions of human life. Not only is considerable time spent going to places, our society relies on transportation to allocate goods to areas of interest. Many professions rely on efficient transportation.

The requirements of safe transportation stem from ability to react in a proper manner to different circumstances. Drivers must pay attention to not only their own vehicle, but to others and environment around them. Some parts of this process have begun to be automated. Different methods of assisted steering, cruise control, anti-lock breaks, and automatic breaking are examples.

If this trend holds, as is predicted, wheel ground transportation will become increasingly automated.

The car now engages in the sense-think-act cycle, through the use of its sensor suite, actuators, body, environment, and processing components. Due to the increased autonomy, contemporary cars can be viewed as robots.

2.2.6 Multidisciplinary

Robotics involves the intersection of several fields of study, as shown in Figure 5. The hard sciences are well-represented, pulling heavily from foundational theories of mathematics, computer science, physics, and engineering. However, construction of a system is not enough, successful deployment is of special importance to robotics. Developing a system that works, but is not intuitive to control or harmful to users and the environment is essentially moot. Work in the social sciences becomes directly applicable to robotics in solving the problems of deployment and use.

The disciplines of academia all have direct relevance to the history and future evaluation of robotics. Academia is split into five main camps:

1. Natural Sciences

2. Social Sciences

3. Formal Sciences

4. Applied Sciences

10 5. Humanities

Natural Sciences: The natural sciences attempt to understand how the ecosystem and its inhab- itants work. Biology, Chemistry, Earth Sciences and Physics make up the sub-disciplines.

Biology has provided inspiration for robot designs. Gaits and physiology of the animal kingdom have motivated the physical design of robots. For example, bipedal and quadrupedal robots try to mimic the real world functions of their animal analogues.

Physics establishes limiting factors that guide robotics. It must be know how material will in- teract each other in order to design effective robots. During simulations, accurate results can be achieved by implementing suitable physics models.

Social Sciences: So- cial sciences attempt to formalize systems in which humans are involved. Psychology, economics, and politi- cal science all impact the way the robots are used in the world.

Psychology provides insight into human cog- nition and behavior.

With many sub-disciplines, psychology gives clues to how and why hu- mans act as we do. Figure 5: Academic Disciplines of Robotics Robotic systems have a ranging degree of hu- man involvement, from operation-of to cooperation-with. Robot-like machines, such as cars, can be

11 the objects of affection from humans. It is not uncommon to see someone who cares deeply about their vehicle. The understanding of the phenomena can point to how humans will interact with robots. Firm psychological principles guide robotic developers in directions that allow for efficient human interaction.

Economics gives incentive, for both good and bad reasons for robotics to expand into new territory. By applying theories of economics, one can gauge risks associated with taking on new endeavors.

Political science, which studies governance and power dynamics in society, may seem far removed from robotics. However, with robots being used in more and more applications, nation-states and other actors see fit that robots can be used for protection and a form of soft power. Actors with robots are seen as more powerful on the world stage. This can be seen as analogous to the space race, which placed countries against each other to be the first to achieve space travel.

Formal Sciences: The relation between the formal sciences and robotics is much more clear.

Robotics, nested in formal sciences, utilizes work done in sub-disciplines such as mathematics,

Computer science, in many ways, is the the result of fruits born from mathematics and statistics.

In addition, computer science brings in electrical and mechanical engineering. Robotics is dependent on applying advances in computer science to improve functionality; they are intimately related.

Applied Sciences: Applied sciences, such as healthcare, ergonomics, and engineering motivate and steer robotics. Engineering clearly is required for design and applications of theories into robotic systems.

Ergonomics provides an extensive and growing body of research for how systems with human factors operate. Design should be influenced by human factors or else be subject to repeat design mistakes of the past. See Section 2.6 for a full discussion of human factors.

Healthcare, like political science, motivates applications for robotics. Many problems with in healthcare have been solved or mitigated thanks to robotics.

Humanities: Humanities, seemingly furthest removed from hard sciences, also have an influence on robotics. Lessons from history and literature shape our conception of how robotics work and their place in society.

More direct, however, is the influence of law and philosophy. Law establishes the rules of so-

12 ciety. It limits how we act, but does not prevent action. The ways robotics can cause harm or disregard human rights are cumbersome. Law attempts to guide development and application in safer directions, such as with autonomous weapons bans.

Philosophy is less concerned with prevention and deterrence, and instead attempts to answer questions of what we should do. This can be answered in a pragmatic sense through formalized reasoning or morally via normative ethical theory. See Section 2.8 for a full discussion on how philosophy guides robotics. Together, these disciplines all meet in the application of robotics. While new to the science, society has been working towards ways of incorporating robotics.

Beyond technical hurdles, problems in robotics are often linguistic, requiring a formalized man- ner of conceptualizing an environment for both humans and the robot itself. A sort of “common sense” is required [6] .

2.2.7 Robotic Systems:

Robots have been applied in nearly all imaginable domains. If a sector of society hasn’t yet seen a robot, they likely will soon.

All types of robotic systems have some degree of human in the loop. The role of the human and the interaction between the robot and the human vary. See Section 2.7 for discussion of human-in- the-loop systems.

Industrial Robotics: Robots that solve manufacturing challenges are considered to be industrial robotics. The Unimate, previously discussed, Figure 3 is an example of industrial robotics.

Maintenance Robotics: Robots that are responsible for the upkeep of robotic and non-robotic systems are maintenance robots. Ensuring that environments remain safe and functioning are defin- ing features of maintenance robotics. Monitoring environmental conditions such as temperature values and stress on system components make maintenance robots the replacement of repetitive, dull, or dangerous tasks that humans are not necessarily equipped to excel at.

Rescue Robotics: In an attempt to remove the humans from dangerous situations, rescue robotics allows for a safer role for humans. First responders like fire fighters, police officers, and bomb disposal specialists place themselves in risky situations to mitigate effects of emergency situations.

By having a robot that can serve as a rescue officer can save countless maiming and lives. The nuclear disasters of Cheronobyl and Fukishima are clear examples of this. Human lives were put

13 directly in danger because robotic systems have not being able to perform relatively mundane operations like turning valves and shoveling debris. However, as this clear use of robotics has been identified, rescue robotics has been able to save lives and remove responders for direct danger. An example of this is the use of a RoboCup robot to help rescue those trapped in the aftermath of a

Mexican earthquake. Significant state funding is going into development and implementations in robotics for this purpose.

Medical Robotics: Due to the ability for robots to perform tasks with extreme precision without the fluctuations associated with humans, the medical field has seen a need for robotic applications.

But other medical uses for robotics have been identified, such as healthcare robots that function similar to nurses.

Social Robotics: Companionship is essential to the human experience. We have seen several examples of the ways technology can influence our social relations, such as online dating and communities. By having a physical system interact in a meaningful way, robotics is an unique position to continue this trend. Robot friends, lovers, and the like have all been discussed, and seem to be nearby. Still, limitations in cognitive AI do not allow for a truly human-like experience.

Also, designers of social robots must pay careful attention to prevent their creations from falling into the realm of the Uncanny Valley.

Ground Robotics: The environment of a robot varies in relation to its task. An obvious appli- cation of robotics is to navigate the world using the ground. Ground robotics simply restricts the domain of operation to the ground itself, much like how humans were limited to the earth until navigation by air or sea became possible.

Marine Robotics: With the majority of the Earth being water, and largely unexplored, marine robotics provides an exciting application to new terrains. Using water as a medium of transportation is the defining feature of a marine robot. They may be above water systems like boats, or submarine vehicles. Industries like the transportation of goods and services, mainly reliant on the oceans for moving from place to place, stand to gain much from full scale marine robot applications.

Aviation Robotics: The last medium of transportation being ground and water is that of air.

Aviation robotics applies the work in aeronautics to provide robotic systems with a new way of navigating. The use of an autopilot in an airplane can be seen as an example. However, the

14 more recent explosion of the use of drones for both pleasure and purpose shows the potential of aviation robotics. Navigating in air allows for easier environment predictions. This can be seen in the difference between deployment of autopilot airplane technology versus autonomous vehicles.

There are simply more things to consider when navigating via the ground.

Soft Robotics: The popular conception of a robot is a rugged machine made of metal and other hard materials. Developments in material science have made it so flexible applications of robotics can be made. These bendable and adaptable systems have promise for uses in the medical field, such a drug delivery to specific areas of the body.

Micro Robotics: Robots have tended to be on the larger scale of things. Whether this is design choice or limitations of housing the physical components of the robot, these systems have begun to trend to become smaller in statue. Micro robotics takes this to the extreme, and focuses on creating robotic systems that are incredibly small.

Swarm Robotics: Having robots operate in large grounds but function largely as one entity is known as swarm robotics. The coordination of multiple robots is the defining feature of swarm robotics, inspired by the way swarms of bees behave.

2.2.8 Public Perception of Robotics

Public perception of robotics has tend to followed suit of popular science fiction media. Isaac

Asimov, author of several science fiction books, can be seen as responsible for bringing the concept of robotic agents to the public conscious. His book, I.Robot establishes the first formalizing of an ethical theory of robots in the Three Rules of Robotics[6]. Asimov continued to portray robots as realistic moral agents in subsequent books and stories.

The trend of treating robots as agents has been a continued trope. The roles the robot vary from protagonist to antagonistic, with an equal amount of personality variation as is seen in humans. As a result, public perception of robots has been rather focused on the artificial intelligence of these systems, and not more accurately a reflection of the physical functionality.

Worries of robot revolution and dominance populate both the big screen and our psyche. These conceptions are not founded in reality, where actual research is leagues away form such dismal conceptions.

In reality, our current robotic systems aim to solve logistic problems and remove humans from

15 danger in a variety of applications. One ought to remain optimistic about future outcomes and avoid falling prey to sensationalized fictional depictions of robotics.

2.2.9 Robot Ethics

Robot Ethics is a subset of machine ethics, which studies the ethical implications of interacting with machines. However, here the distinction between robot ethics and roboethics is made. Roboethics focuses on questions associated with making robots moral agents in their own rights. This involves the giving rights and assigning responsibility to man-made machines. Ethics considering emerging technologies like this is still in its infancy, with no real-world examples at this time. In order to be considered a moral agents, a degree of unprecedented autonomy is required. This is beyond our current autonomous robots capability, where higher level reasoning is not available.

2.3 Telepresence

Establishing the necessary and sufficient conditions of “presence” gives an insight into what telep- resence is. The scope of what is considered telepresence is broad, but centered around the concept of remote awareness. The following section places the concept of telepresence within the history of technology and provides a set of general telepresence features.

2.3.1 History of Telepresence

Naming

In a funding proposal in 1979, from the suggestion of a friend, Marvin Minksy coined the term

“telepresence” [29]. In a following article in Omni magazine, Minksy elaborates on the concept of telepresence. He saw the use of technology as a medium for presence, something which allows for experiences of places remote to a user.

However, the concept of co-presence, a foundational principle of telepresence, was formalized by Goffman as:

An individual’s sense of perceiving others as well as the awareness of others to perceive

the individual.

Philosophy of Telepresence

Telepresence, while often framed in context with robotics, is not restricted to that domain. The experience of telepresence simply requires an external medium transform consciousness. It is a

16 result of both the technology used and the user [46]. There is no need to restrict the medium of technology to something highly advanced. More simple technologies can be sufficient, such as the letter from a loved one. The recipient of the letter, upon reading, experiences a sense of removal from their local selves. They may remember one specific event reference and have the same feelings as before. Vision is not required to prompt a telepresence experience. When re listening to the live recording of a recently attending concert, it is easy to be taken back to that time and place, phenomenologically reconstruct the event in one’s mind, to the best of their ability. Recalling other modalities, the sights seen or smells encountered. It happens to be that telerobotics fits well for the expansion of telepresence into a more direct technological medium[40].

Early Work

Early telepresence work assumed an anthropomorphic design was a key for effective telepresence.

That, in order to BE elsewhere, the remote system must both look and operate similarly to its human user[11].

Immersion: Immersion is often used in describing telepresence experience, but the two are not synonymous[11]. Biooca and Delaney (1995) define immersion as:

The degree to which a virtual environment submerges the perceptual systems of the

user in computer-generated stimuli.

The set of physical properties that match the fidelity of real experiences are the immersive factors. Immersion, thus, is objectively measurable.

The response elicited by immersion is the experience of presence, formalized by Lomard and

Dilton 97 as: “extent to which a person fails to recognize, perceive, or acknowledge the medium.”

In fact, telepresence allows for a level of immersion that can be achieved using video-only systems

[47].

Supervisory Control: As autonomy of a system increases, the role of the human shifts from full operation to supervisory control. An operator no longer is responsible for individual events, instead, they must remain vigilant of the entire system’s performance. Sheridan first defines this control of robot systems as[41]:

Emphasizes that human operator supervises a lower intelligence embodied in the tele-

17 operator itself by intermittently monitoring and reprogramming as necessary for either

routine or emergency situations.

2.3.2 Psychology of Telepresence

Cognitive Factors: Pstoka and Davison identified two main categories of cognitive factors for telepresence: the quality of immersion, and an individual’s susceptibility to telepresence. Immersion, being the result a set of physical properties, can be adjusted to match an individual’s characteristics.

Individual factors can largely determine to what degree presence occurs and what the strength of the experience is [11]. Maximizing these immersive factors would result in a more immersive experience for someone, however, differences in psychological makeup amongst people can account for there being differences in telepresence experiences. Additionally, in order to fully actualize the telepresence experience, the participant must be willing. Remaining to be cognitively dissident will never allow for full immersion into telepresence.

Situational Awareness: Situational awareness is the degree to which a person is familiar with their environment [13]. Telepresence can be seen as a loss of situational awareness of the local environment in exchange for an increase in situational awareness of a remote space[11]. Spatial ability of an operator is highly subjective [25].

Recently, the role of situational awareness has been included on cyberspace. Cyberspace, con- ceptualized as any computer network-related activity, included all levels of interaction with the

TCP/IP stack[14]. The Australian Cyber Security Operations Centre, as one example, has found

SA to be an essential measure of response to cybersecurity events. Maintaining vigilance of large and diverse networked systems is quite challenging, thus a full understanding of situational awareness can help mitigate future attacks by necessitating quick response[14].

2.3.3 Telepresence and Teleoperation

Telepresence was conceptualized within reference to teleoperation[11, 29]. Minsky described telep- resence anecdotally to how a teleoperator has a sense of displacement when working remotely.

Remote operation is the defining feature of teleoperation, where a user manipulates a system that is not necessarily located nearby. As a field, teleoperation has been a research subject since early

20th century[15], conceptualized as “work from a distance”.

In addition to being remoteness, a teleoperation system also must be real-time, are close to

18 real time. This is in contrast to high-level telerobotic systems that act via master-slave, with the humans assigning goals for the robot to execute with no fine-grained control.

2.3.4 Modalities

The combination of different modal interfaces can also impact the degree of effectiveness of a telepresence system. Vision is the most common modality of telepresence. For obvious reasons, one relies on vision as a significant contributor for spatial awareness [25]. Including additional modalities help resolve issues in telepresence associated with attention switching [5].

Audio information display can be as important as vision when it comes to source attenuation and spatial intelligence. Through the use of speakers, immersion can be increased, prompting a more believable experience, see Section 2.4.3.

2.3.5 Telepresence Design

Telepresence design takes advantage of multi-modal interfaces, and thus has several methods of passing information to a user. Design is highly subjective to what the intended experience is, the hardware limitations, and user differences. However, a generalized approach to telepresence design is possible.

Seven design dimensions have been identified for telepresence by Rae et al [34].

1. Initiation How local and remote users begin to interact

2. Physical Environment The area in space that the system inhabits

3. Mobility What does the remote operation area look like

4. Vision What will the users perceive and focus on

5. Social Environment Impact of direct and direct relationships to the system

6. Communication Interaction between users

7. Independence What is the level of autonomy that the system is capable of

Information passing of a multimodal interface may not be secure or private. Thus, to properly design a multimodal interface that protects the user, a designer must be aware of what information is produced and how it is passed [35]. For example, speech commands can be seen as a lack of

19 privacy. Developers of telepresence systems must be aware not only of the user experience, but the also his or her privacy.

2.3.6 Applications of Telepresence

Telepresence can be applied to nearly any area in which someone’s sense of self is relevant, which nearly encompasses the entirety of the human experience. Robotics is one immediate example, but is not the only one.

Humans have an incredible capacity to exert their situational awareness into objects in the world that are outside of their own body. This ability can be harnessed to create telepresent experiences in a variety of domains. Telepresence is often used as the control medium of telerobotic system that share the master-slave architecture. Commands are issues to the robotic system and then executed, the human does not control the robot. However, when control is handed over for a human to teleoperate the system, principles of telepresence have a more significant contribution.

Entertainment is often defined in regards to escapism 1. One watches a movie to feel as though they inhabit an alternate reality. Media serves as a great way to live as someone else. Telepresence is in a unique position to establish principles of how this is achieved. The passive view of a screen is often enough, but by applying theories of telepresence, entertainment stands to gain from newfound degrees of immersion.

Education can often be seen as boring or uninteresting. Engagement is often the focus of lesson plans, so as to ensure that the audience pays attentions and retains the information. Telepresence can help increase levels of engagement in a variety of manners. One that immediately comes to mind in teaching chemistry. By applying the principle of telepresence, it is possible to design an experience to become the molecules. By donning virtual reality equipment, ones perspective can be shrunk to the microscopic scale and allow for more interesting lessons not previously possible.

In fact, the ability to reconstruct scale, rather large or small, allow for telepresence to increase engagement and remove layers of abstraction.

Medical robots apply theories of telepresence to have remote operating possible. Through telep- resence, the medical field stands much to gain by applying a computerized medium to represent abstract concepts. It is much easier to understand something with visual aids.

1This is an over simplification of a long discourse in aesthetics, which searches for the philosophical foundations of art, beauty, and the like. However, art as escapism is one vein of thought in the field.

20 2.3.7 Drawbacks of Telepresence

Time Delay: Physical limitations do not allow for a truly latency-free transmission of data [15].

Thus, all systems will have to compensate for some degree of latency, making it one of the most challenging hurdles of deployment. However, the limiting factor is the speed of light, and that only going becomes relevant at the planetary scale.

2.4 Virtual Reality

Virtual Reality is abstract experience that is separate from cyberspace. Its applications and history are motivated by visions of the future, but remain grounded in our actual reality. Here the theory of virtual reality is explain in an historical context and its relation to telepresence is shown.

2.4.1 History of Virtual Reality

Definition

Jaron Lanier, first used the term to describe the extension of sensory perceptions through techno- logical medium. User behavior is induced via artificial sensory stimulation, with little awareness of the medium [26]. Visual and audio displays create a sense of immersion for a participant. Matched motion of the user is often mapped to the simulation, but motion is confined to specific area where sensors can gather corresponding information.

Interactivity: Similar to other systems, interaction occurs in two different capacities, open and closed loop. User actions have no impact on the sensory simulation in an open-loop system. Ex- amples of this are in IMAX move theatres, where the experience is not altered as a result of interactivity.

A closed loop virtual reality system depends on a users interactions. Simple examples of this are in the cheaply available Google Cardboard, where a user holds the device to their head and rotates to see different scenes, exhibiting matched motion. More complex examples utilize additional interactivity features to further expand the experience.

Early Prototypes

However, this new term is also an extension of previous efforts in Head-Mounted Display information mediums.

The first HMD mediated sensory experience is often cited as Ivan Sutherlands’ 1968 experiment,

21 see Figure 6.

Virtual Environments and Virtual Reality

Virtual Environments (VE) research is used synonymously to virtual reality research. VE and VR battled for position in the official lexicon, with MIT supporting the former, but VR having a wider appeal.

The more formal sounding virtual environments sought to avoid falling

down the same path as “artificial intelligence” once had. AI, upon for-

malization, was promised to achieve human-like intelligence in a quick

manner, and usher in a newfound age in machine intelligence. Now, it is

clear that AI has not achieved the early promises, but hopes are still held

high. Virtual Environments does not have the same sense that virtual

Figure 6: Sutherland’s reality does. However, the promises of virtual reality have been made

Sword of Damocles much more hesitantly than AI, keeping what is possible currently at the forefront.

Early VR experiences, beyond those of Sutherland, focused on incorporating additional modal- ities in increasingly intuitive ways. Today, VE research is read in a similar vein as VR. All work done under the vein of VE is applicable to virtual reality efforts.

Virtual Worlds Virtual worlds are the space in which the user inhabits while using virtual real- ity. Synthetic worlds are computer-generated environments, resulting from simulated physics and geometric primitives[26], akin to video games. These worlds vary in detail. Modern imaging allows for digital reconstructions of a real-world environment, known as a captured virtual world. Levels of immersion can be increased, and spatial representation of real world places is able to be achieved.

However, a synthetic world need not be fictional. Recreations of real-world environments fall under the category of synthetic worlds. The distinction between synthetic and captured virtual worlds lay in the use of actual images versus computational geometry.

2.4.2 Applications of Virtual Reality

Virtual Reality found a natural use in the extension of teleoperated machines to telepresence robotics. Additionally, application of VR expand beyond the pragmatic uses of robotics into both entertainment and education.

22 Virtual Reality in Education

Section 2.3.6 described the benefits telepresence presents to education. The overlap between the two are significant, but virtual reality has additional uses in education. VR has been used for training since it first was introduced. Training for Air Force pilots has been done via simulation, with certain qualifications requiring pilots to pass simulation training. Due to the large cost of military aircraft, and the low tolerance for error, virtual reality flight simulation will remain a fixture. In fact, any system that shares the same characteristics of low error tolerance and high cost can benefit from

first educating its users via virtual reality.

Virtual Reality in Entertainment

VR as entertainment can be found as a catalyst for the recent boom in VR research. It has garnered the attention of both laymen and academics, with applications in various domains.

Video Gaming: Over the last few years, several video game developers have begun to create games centered on the virtual reality peripherals. Increases in hardware performance has allowed for cheaper consumer-grade products. For the enthusiast, building a high performance gaming computer is a common practice in the contemporary gaming community. It is also the case that modern pre-built laptop and desktop computers are being advertised as VR-Ready. In addition to computer gaming, console gaming systems like the Playstation 4 Pro have additional hardware accessories for virtual reality. Playstation VR is among the first well-accepted console virtual reality gaming platforms, but it far from the first. Nintendo Virtual Boy was an early attempt to profit off of the public’s newly found infatuation with virtual reality. The system had a relatively poor market performance, due largely to the limitations of the hardware. Only two colors were available and it required the user to lean into a VR viewer, similar to a head-mounted display.

Virtual Reality in Research

Consumer grade products allows for easier access to research materials, at relatively cheap prices.

This is much better than having to construct VR systems and also affords the opportunity to use a large amount of development resources.

Use of Gaming Hardware: Current research in virtual reality is taking advantage of the mod- erately priced gaming peripherals. Systems developed by Occulus and HTC allow for the university researchers to expand into virtual reality. Many labs are equipped with adequate computing hard-

23 ware, limiting the barrier of entry. Development software is also increasingly available to researchers and designers. Steam VR, the de facto virtual reality gaming software allows for Unity developed projects to be easily deployed.

2.4.3 Types of Virtual Reality

Thinking of virtual reality conjures up images of users donning headsets with on-lookers removed from the experience. However, Virtual Reality is not bound to traditional Head-Mounted Displays.

Information display in virtual reality can be fixed on either the user or the world [26].

Visual Display

Vision is the primary modality of information presentation for virtual reality. Just as in waking life, our sense of vision heavily influences the way we perceive the world. Distortions or disruptions of vision cause experiences that stray from the norm.

User-Fixed Displays: User-Fixed Displays track a participant’s motion and orients information likewise.

World-Fixed Displays: Unlike HMD VR, CAVE-VR allows for a user to remain a physical connection to their body and preserving their mind-body relationship.

Aural Displays

Aural displays present audio information to a user via two methods, 1.) User-Fixed Displays and

2.) World-Fixed Displays.

User-Fixed Displays: Often coupled with user-fixed visual displays, aural displays can be respon- sive to a participant’s interaction. Headphones remain placed on a user’s ears, providing privacy of an experience [26].

World-Fixed Displays: Just as a user can enter a physical environment have a visual virtual reality experience, world-fixed aural displays give user’s a non-intrusive audio experience. Incon- spicuous speaker placement with efficient audio recreation can create the illusion of sound coming from the visual display itself.

CAVE: Walk-in, and intrusiveness, CAVE allows a user to immerse themselves in a VR experience by enclosing them in a room where the walls are screens with stereoscopic audio. With high fidelity images, the user feels as though the are in much larger environment, forgetting they are bound in

24 a physical space.

2.4.4 Drawbacks of Virtual Reality

The human body was not designed to have sensory information presented in a restricted techno- logical medium as is the case in virtual reality. When we navigate the world, we do so lag-free with complete control of our actions. This is not the case in virtual reality. As a result, virtual reality can cause psychological and physiological discomfort to a user. Research in perceptual psychol- ogy has elicited information of how the brain processes this simulated experience into perceived phenomena[26].

Simulator sickness and cybersickness, while similar in symptoms, have different causes. When using Head-Mounted Display Virtual Reality, the resulting ailments are considered cybersickness

[36]. In fact, cybersickness can be three times more severe than simulator sickness [44].

If an onlooker were to get ill, or another medium were used, this would be considered VR or simulation sickness. Virtual reality sickness exhibits a different symptomology than cybersickness

[44].

Psychological

HMD VR has been debated as harmful to the human psyche do the the disconnection between the user’s sensory experience and their body. Enough time in this has been found to lead to Alternate

World Syndrome. This can be quite disturbing to an individual’s psychology in that they are not fully situated in reality. There exist gaps between what is real and what was shown which can take some time to overcome, eventually returning back to normal[47].

Regardless of application, VR has also been discussed in connection to Plato’s Allegory of the

Cave. More discussion on this is included in Section 2.8.

The role of place and grid neurons of the brain provide the psychological underpinnings of the virtual reality experience [26]. These neurons are responsible for spatial intelligence, recording the environment and creating a sort of map within the brain. Place and grid cells that have more correspondence to each other both are thought to be the encoding of the that place. One does not have to be physically present to imprint on these neurons, instead the same encoding can be done via a technological medium such a virtual reality.

Fatigue is a commonly reported symptom of prolonged virtual reality experiences. It is thought

25 to be the result of forced fusion in the perceptual system. Additionally, nausea prior to the virtual reality experience is an indicator that one might become ill [4].

Physiological

VR has been associated with several different ailments, when used for an extended period of time.

Alternate World Syndrome (AWS) was an early identified result of VR use. Due to the similarity of VR to simulators, such as flying simulators (that do no qualify as virtual reality), VR also shares ailments with extended simulator use. The most notable of these are Simulator Sickness Syndrome.

VIMS, distinct from SSS, also affects users.

Explanation of Drawbacks

Vection is a potential cause of these ailments, but recently more attention has been given to postural disturbances as the primary cause. A VR system is only as usable so far as it limits the adverse symptoms. It is essential to understand what causes these symptoms in order to build more robust designs.

Sensory Conflict Theory: One leading theory of the causes of VR induced sickness is Sensory

Conflict Theory. It posits that due to mismatches in sensory processing and expectation, experiences in virtual reality create confusion. Sensory Conflict Theory is often held as the most adequate explanation of some of they symptoms of VR or simulation use [7]. Signals received by the visual system, vestibular system and nonvestibular proprioceptive systems sometimes conflict with each other, causing imparement and the rise of adverse symptoms. Mainly, the conflicts with the vision system with others causes this [36].

Vection: Vection is the feeling of moving though space that is produced by the visual system and is measured in Vection Occurrence Rate (VOR) [48]. It has been shown that people can be immune to vection-caused sickness [26]. One classic example of vection is in the room rotation experiments.

Users were suspended on a swing in the center of room that rotates around them. The apparent motion induces motion sickness-like symptom.

Asthenopia: The symptoms of eye strain, headache, and blurred vision are collective identified as asthenopia [20]. Display design and quality of the images (pictoral fidelity) are causes of these symptoms.

26 Postural Stability: Postural instability is medically deemed ataxia [20]. It has been shown that keeping a stiff posture can reduce the occurrence vection and other sickness. This is because there appears to be no lag between the user’s movements and the visual display, due to the user staying still.

Motion Sickness: The common analogue to virtual reality-induced sicknesses is that of motion sickness. Often experienced as the passenger in a car that does not pay attention to the moving environment around them. Motion sickness is caused by disturbances in the vestibular system. It is the case that individuals without properly vestibular organs are free from developing motion sickness or motion sickness symptoms. Even remaining stationary does not prevent you from experiences motion sickness [20].

Virtual Reality Maladaption Syndrome: When virtual reality sickness persists, it can manifest into VR Maladaption Syndrome. Whereas VR sickness is short-lived, VR Maladaption Syndrome lasts from hours to days, causing significant discomfort[26]. Women have been found to be more perceptible than men, but the overall indicator of susceptibility is if a person has had it before.

Some of these symptoms can be reduced as the result of more exposure to VR [36]. Through anomalies in the vestibular system, people can be immune to vection [20].

It need not matter what the root cause of these are if they continue to be unavoidable. For the foreseeable future, symptoms will continue to be mitigated as technology evolves to account for the root causes. Hardware improvements that reduce the likeliness of sickness pushes the burden to virtual reality software developers prevent such problems[26].

2.4.5 Measuring Virtual Reality

Quantifying the virtual reality experience is a necessity if the efficiency of the system is wished to be increased. Human testing is required to gather this information, garnering even more importance to get system design and implementation right. A well-known result of having a human required for testing is that system developers often are the primary subjects in informal development exper- iments, leading to a bias as to the efficacy of the implementation [26]. Furthermore, if the system designer is not included in virtual reality experience evaluations, his or her peers are, leading to very similar results compared to having an unfamiliar subject evaluate the system [2].

Overtime, people can build up immunity to the ailments caused by virtual reality. Coupled with

27 the primary subjects being system designers, it becomes clear the need for frequent fresh subjects to receive the best data on virtual reality system performance. A drawback of having subjects become ill is that data on the remaining portions of the experience becomes distant. The ill participant is now less likely to want to engage in another virtual experience.

Objective

It remains necessary to understand, and predict, when a user may exhibit these symptoms. User symptoms can be monitored pre, during, and post experience. Physiological symptoms can be monitored to predict the onset of disturbances.

Subjective

Subjective measures are often gathered after a VR experience to gauge how intuitive and com- fortable the experience was. A user can often deduce whether or not discomfort was experienced, but pinpointing the exact cause of this discomfort is much more difficult. As a result, perceptual training approaches prompt the user to look for certain objects or mismatched stimuli[26].

Intensity of symptoms is an important subjective metric to gather. Individual reports are re- quired to gather an understanding of the spectrum of symptoms. From this data a percentage of users that have experienced a certain threshold of discomfort can be deduced, showing the gener- alized experience as a whole[26].

By presenting or removing certain stimuli as specified times, an understanding of individual features of the VR experience can be made[26].

2.5 Human System Interactions

Humans interact with an extensive amount of non-biological actors that exhibit a wide range of autonomy. Machines, computers, and other embedded systems make up the actors. There are large amounts of factors that impact how humans do so. The following section describes what Human-

Machine Interaction, Human-Computer Interaction and Human-Robot Interaction are and how they overlap with each other.

28 2.5.1 Human Machine Interaction

Definition

Human-Machine Interaction focuses on the ways humans work with machines in a collaborative manner.

Stages of HMI

Human-Machine Interaction has been formalized as taking place in four distinct stages [30].

Intention Stage: The first stage of the Human-Machine Interaction cycle is to form an intention.

Here, a private and personal formation of the desired goal occurs. A mental model of what the successful achievement of the task would look like is established, which will serve to direct future stages of the interaction cycle.

Selection Stage: After the human user has formed his or her intention of what the system is meant to accomplish, the user then selects an action or sequence of actions to enact. This is a translation of the first stage’s mental plan to the accompanying necessary commands. Of the actions considered, they must be possible given the current situation and features of the machine.

Execution Stage: The action or action sequence derived during the selection stage are physically enacted during that subsequent execution stage. The mental results of the previous two stages are translated into physical acts of information entering.

Evaluation Stage: After the human has physically manifested his or her goal intentions, the outcome must be evaluated. This stage is where the cycle is determined to begin again with the formation of new intentions or ends with satisfaction.

The first two stages of HMI are mental, they require no physical action from the system. The execution is the only physical interaction in the the HMI cycle, for evaluation is mental. The evaluations are then placed back into the mental model for the cycle to complete again. These stages transition over to Human-Computer Interaction.

2.5.2 Human Computer Interaction

Origin

Human-Computer Interaction (HCI) aims to study the factors pertaining to use and operation of computer-based systems. Although not yet formalized, HCI can be seen to have begun with the

29 first electronic computer, ENIAC. With the following systems, computing was seen to have entered its first era, the Mainframe Era, characterized by having multiple people to one computer [24]. The following era was ushered in with the invention of the Personal Computer (PC), thus called the PC

Era. One computer per person has been the dominating theme until recently, where people have access to multiple computers at any time, the third era.

The current trend in HCI is to establish multi-modal, interactive and active computing appli- cations [24]. Unimodal interactions are categorized in three areas: Visual-Based, Audio-Based, and

Sensor-Based. Eye-tracking software and computer vision allow for vision oriented interaction to occur, with a system being able to detect, track, identify and analyze a user’s behavior. Audio-

Based interaction takes the form of speech and sound recognition software. When a user interacts with a computer system physically, through mediums such as keyboards and haptic devices they are doing so in Sensor-Based Interaction. By combining one or more of the computer interaction categories together, multi-modal interfaces are created. Multi-modal interaction allows for a more user-friendly experience by more successfully understanding human behavior[24]. Natural to use systems are the result of intelligent interaction, which focuses on understanding how individual factors can be improved for a more intuitive experience [24].

Ubiquitious Computing, shortened to “Ubicomp”, aims to remove the current conception of desktop in exchange for embedded computers and displays in the environment. Also known as ambient intelligence or pervasive computing, ubicomp is seen as example of the third era of com- puting. A user may not know that they are actually interacting with several computers at once if the systems are cleverly disguised.

Principles

The two design principles of HCI pertain to functionality of the system and usability. A system is only as effective as the balance between functionality and usability allows. Furthermore, HCI conceptualizes user activity as three different levels: physical, cognitive, and affective [24]. Physical activity consists of the mechanics involved in interacting with a computer. One must be able to properly act on a system. Cognitive activity within in the frame of HCI is how a user understands and then interacts with the computer system. Affective actions are how pleasure and other attitude changes are given to the user. Work in affective psychology serves as foundation for achieving such

30 results.

2.5.3 Human Robot Interaction

Human Robot Interaction is often considered a part of Computer-Supported Cooperative Work

(CSCW)[50]. Grief 1988 defines CSCW as:

An identifiable research focused on the role of the computer in group work.

HRI is distinct from both HMI and HCI in that:

it concerns systems which have complex, dynamic control systems, exhibit autonomy

and cognition, and which operate in changing, real-world environments [38].

Additionally, a computer generally operates in a deterministic environment, whereas robots often deal in changing environments with varying degrees of harshness [39]. A robot is expected to undergo physical changes such as sensor degradation compared to the relatively subtle physical changes of a computer.

In an effort to establish formality in HRI, Yanco and Drury [49] created a taxonomy with the following categories:

• Autonomy Level/Amount of Interaction

• Ratio of People to Robots

• Level of Shared Interaction Among Teams

• Decision Support for Operators

• Criticality

• Space/Time Taxonomy

• Composition of Robot Teams

Human Roles in HRI

Human-Robot Interaction has four unique roles for humans within the system: Supervisor, Op- erator, Peer and Mechanic. These roles are hierarchical with each position expressing power and control over another, to varying degrees.

31 Supervisor: Robot Supervision was first formalized by Sheridan. The role of robot supervision is akin to the how one human would supervisor another, where the supervisor monitors and intervenes in the system. The duty of supervisor is to create action plans and modify them when necessary

[38].

Operator: Next in the hierarchy is the operator. He or she is responsible for robot behavior performance according to the supervisor’s plan. Short-term navigation and manipulation goals are given and executed.

Peer: The peer role is divided to either the bystander or the teammate. The bystander is a passive actor but still can impact robot performance. The teammate works alongside the robot without directly controlling by giving plans but lacking control.

Mechanic: Lastly, the mechanic physically intervenes with the robot to change behavior. Higher level behavioral intentions are the focus of the mechanic, not low level fine-grained behavior.

HRI Metrics

Metrics for HRI evaluation consider how a human’s time spent with a robot can be quantified for further understanding. Much like how human factors focuses on the performance of the system, so too does HRI.

However, it is not likely that a generalized metric that is applicable to all systems would be found, a combination of several metrics can point to overall system performance [9, 8].

Figure 7 shows the five areas which have been identified to encompass Human Robot Interaction

[45].

1. Navigation

2. Perception

3. Management

4. Manipulation

5. Social

32 Figure 7: Areas of HRI Both the humans and the robots cooperate and share re- sponsibilities associated with each area.

Quantitative Metrics: Olson and Goodrich have es- tablished several metrics that are pertinent for under- standing an HRI experience at the system level [31, 16]:

• Neglect Time

• Task Effectiveness

• Neglect Tolerance

• Robot Attention Demand

• Free Time

• Fan Out

• Interaction Effect

Neglect Time: Neglect Time (NT) is a measure of the degree of autonomy a robot exhibits, essentially, how long can the robot operate without intervention[32]. Often, NT is the elapsed time between two human-given interventions. Increasing autonomy is assumed to increase neglect time[32]. Trust has an influence on NT [18]. As an operator affords more trust into his or her system, they can afford to “forget” about it for a period of time. However, it is not always best to have a large amount of NT, for it may be difficult to reengage with the robot. Conversely, too little of a NT and the operator’s time is not efficiently used.

Task Effectiveness: Task Effectiveness (TE) determines how efficiently a task was performed.

This is calculated by the duration it takes to complete a task and how well it was completed.

Neglect Tolerance: Neglect Tolerance (NT), longitudinally related to TE, measure how much

TE is reduced over time. This is represented by a neglect curve.

Free Time: The fraction of time during a task that human does not have to pay attention to the robot is measured by Free Time (FT).

FT = 1.0 − RAD (2.1)

33 Robot Attention Demand: Opposite of Free Time, Robot Attention Demand (RAD) is a mea- sure of how much time a particular robot requires of a human counterpart.

IE RAD = (2.2) IE + NT

Fan Out: Fan Out (FO) determines how many robots a human can control at one time[32, 31].

1.0 IE + NT FO = = (2.3) RAD IE

FO is often found to be around four to six robots at a time [9].

Interaction Effect: Interaction Effect (IE) is a measure of the time required to interact with a robot.

HRI Principles

The above metrics can be used as the basis for forming principles for HRI. Goodrich and Olson have identified seven principles of HRI, specifically based on NT, IT, RAD, FO, and FT. Human

Robot Interaction is based on the following principles of:

1. Implication Implicitly switch interface and autonomy modes

2. Naturalness Let the robot use natural human cues

3. Omnipotence Manipulate the world instead of the robot

4. Robocentric Manipulate the relationship between the robot and the world

5. Humancentric Let people manipulate presented information

6. Externalization Externalize memory of the human

7. Helpfulness Help people manage attention

Operator Performance HRI Metrics

Operator performance in HRI is measured in two different capacities: Situational Awareness and

Cognitive Workload

Situational Awareness: Defined in section 2.3.2, SA becomes relevant in context with HRI due to the need to select non-intrusive workload measures that represent the internal happenings of an operator [45]. SAGAT is the leading metric [12].

34 Cognitive Workload: Defined at 2.3.2 Unlike situational awareness, there is not a need for non- intrusive measures of cognitive workload [45]. NASA-TLX is a measure developed to determine how much cognitive workload is required of a user.

Human Robot Teams

Human Robot Interaction is not limited to only one robot and one human. As is clear in the inclusion of metrics such as Fan Out and Robot Attention Demand, which specifically target multiple robots.

HRI sees the expansion of the control of robot team as a main goal of the field [32], with the assumption that efficiency of an operator can increase as the number of robots increases. Five similar tasks can be dealt with simultaneously by five separate robots only if the operator can properly control all team members. A Human Robot Team consists of a single operator that either controls or supervises several robots [9].

Goal-Directed Task Analysis: The purpose of Goal-Direct Task Analysis is to provide an un- derstanding of what information an operator needs to properly complete a given goal [2]. GDTA aims to facilitate the creation of new techniques and methods for HRI by focusing on the specific re- quirements and needs [2]. Operator basic goals are deduced, allowing for a full understanding of the situational awareness constraints and user requirements. It is necessary for a firm understanding of how information, interactions, and available capabilities to accurately assess HRI requirements[2].

2.6 Human Factors

Human Factors, or ergonomics, examines what impacts the performance of humans within larger systems.

2.6.1 Definition of Human Factors

Human Factors (HF) is the “study of the variables that influence the efficiency with which the human performer can interact with inanimate components of a system to accomplish the system goals” [33].

Ergonomics is the contemporary use of human factors in the current lexicon. However, there is no difference in use of the terms. Due to historical precedence, the term ‘Human Factors’ will be used throughout this paper, often abbreviated to ‘HF’.

Even though HF has existed for quite some time, there does not exist an easy way to predict

35 which factors are the most important and their interactions. Human Factors remains an important

field for evaluation of how a system’s performance, safety, and efficiency can be impacted by human- oriented design.

Origin

Human Factors emerged as a need to study how to mitigate errors associated with human use of systems. The central concept of HF is of the system. All measures are of interaction between a human user and the system itself. In fact, the user is viewed as part of the system. The system exists for the purpose of achieving some goal.

System Structure: Systems, being inherently hierarchical, require their components to have input and outputs. It is the view of HF that a system’s performance is the result of the design and components. If a system shows deficiencies in performance, the structure and make-up is to blame.

There are two kinds of goals: mission-oriented and service-oriented [33]. System structure also describes the information and action relations of a system. Any given system can be either parallel, serial or a combination of the two. In parallel systems, separate tasks and channels of information processing can be performed simultaneously. In serial systems, performance is linearly restricted, required on step to follow another. In hybrid systems, different functionalities are partitioned to subsystems, allowing for more efficient performance.

System Reliability: Reliability Analysis measure the individual parts of system to access the overall confidence that can be placed into it. Reliability, itself, is the “probability that an item will operate adequately for a specified period of time in its intended application”. System reliability is separate from human reliability, where performance is a result of subjective differences in users.

For example, how well the operator deals with stress. System reliability is the relation between the human and the overall system goals. Metrics for this are gathered via computational methods or

Monte Carlo methods.

2.6.2 Role of Human Psychology

Psychology attempts to answer questions related to human behaviour and cognitive functions. By utilizing the current paradigm, human factors relies heavily on psychological principles in order to properly design and implement systems.

36 Information Processing

Just as in other systems, humans consist of several subsystems for different functions. Similarly, these subsystems can act serially or in parallel, with complex systems being hybrid.

When faced with multiple different information sources to attend to, humans work cyclically, or linearly [2].

Three Stage Model: Classically, human information processing is thought to be a result of a three step process.

I.) Perceptual Stage: In the perceptual stage, sensory organs are stimulated, resulting in a process. The sources of simulation are detected, separated from conflicting sources, and iden- tified. The quality of the stimulation determines how much information can be utilized. Unclear information received does not allow for efficient information extraction, limiting performance.

II.) Cognitive Stage: During the cognitive stage, information gathered during the perceptual stage is processed to determine an appropriate response. Groome and Eyesenk, 2016, describe this stage as including

• retrieval of information from memory

• comparisons among displayed items

• comparisons between displayed items and memory

• arithmetic operations

• decision making

Human Factors tries to systematically remove limitations associated with these resources. This often includes additional displays of information.

III.) Action Stage: The action stage, then, is when humans perform tasks based off of what was perceived during the perception stage. Behavior is manifested into physical events. From here the perceptual stage is repeated by observing the response the behaviour has elicited.

2.6.3 Human Factors Summary

This has just been a small snippet of the ways human factors can guide the future of robotics.

Robot developers need to think of the way humans role in the system at large. They must be aware

37 of the differences in how humans may use the system, and the ways in which it may impact them.

The large history of work in human factors can be migrated over to robotics with minimal changes in implementation. Design of user interface and experience rely heavily on human factors theories, to disregard them would be negligent.

2.7 Human-In-the-Loop

When humans are part of the performance of a system’s decision procedure, we view them as “in the loop”, meaning they are part of the overall functioning of a the system.

What motivations do we have for keeping humans in the loop of systems? We know of their unpredictability and proclivity to make mistakes. It seems like the best course of action is to automate away humans in exchange for more flexible, predictable, and controllable systems.

Additionally, it is the case that humans exhibit cognitive biases. Biases arise in our interpretation and perception of events. Our internal representation of the world can be drastically different than the facts that make it up. These biases can express themselves in behavior, causing sometimes unpredictable actions.

Other humans are able to handle this unpredictable behavior by relying on instincts and their own theory of mind. If the observed behavior would lend itself to patterns, perhaps based on past actions, models could be form of the potential outcomes. The apple falling from the tree, with nothing around, will inevitably hit the ground.

But we have learned how to use a theory of mind and intuition to handle circumstances that are not clear in their determinism. This ability is unrivaled. Unlike physical action or predictable computation, no creation of ours, besides our offspring, have been able to surpass this 2.

Human-in-the-Loop systems (HiLs) require some subset of functionality of the system be per- formed by human counterparts. The functionalities performed by the humans are of varying degree of physical and cognitive methods.

2.7.1 Definition of Human-in-the-Loop Systems

HiLs have humans perform physical tasks at a level equal to the capabilities of that system. Tra- ditional teleoperated robots would have humans perform all physical control requiring higher level

2Artificial Intelligence is often talked in context of general intelligence, or AGI, which views achieving a human- level of of conceptualization as its goal. AGI, then, could feasibly surpass humans in their use of theory of mind to predict seemingly unpredictable behaviors.

38 cognition. These early systems did not have a high degree of autonomy, thus, their human com- patriots would perform remote control of navigation through terrain. Then, depending on the manipulation functionalities of the system, the humans would also perform dexterity tasks, such as controlling ans manipulating an arm of the robot. A human is responsible for all the cognition behind the decisions of the system.

Remote and Local Applications

Humans cooperating with systems can do so in either a local environment or a remote one. Local environments give the advantage of being in immediate presence of the system. Observations of the system’s functioning would not be contingent on a sensory system and data transmission like in a remote environment.

When operating remotely, the human has advantages that come along with physical separation, most notably that of safety. Remote operation allows for a human to be comfortably situated far away from danger, either known or unknown. In addition, remote operation allows for humans to access locations not previously possible. Large, small, or difficult to reach goals are able to be accomplished, with the human providing critical insights to the system.

2.7.2 Security Critical Functions

As systems become increasingly autonomous, HiL requirements lessen to those that are security- critical functions [10]. This may involve different combinations of identification, verification, and execution of tasks. Essentially, permission of the human operator is necessary for the system to move forward.

2.7.3 Performance Factors

A robotic system is often characterized by have the ability to see, think, and act. Seeing involves sensing the environment. This is achieved through the coordination of sensors.

Thinking is the decision making aspect of a robotic system. Utilizing information gathered from sensors, a robot must plan what the next appropriate actions are, given its goal and the physical limitations of the robot.

The acting part of the cycle is execution of some action as the result of the plan provided during the thinking portion. A robot performs these three functionalities ad infitum. Each of the functionalities may vary in degree of complexity and efficiency due to the exact implementation

39 and design of a the robot.

Regardless of the degree of autonomy a robotic system posses, there will be a human involved somewhere in its application, whether that be the complete control of the system or approval to deploy the system.

As several security breaches are result of human error, system design best take heed of the importance of well-designed and though-out approach. Well-chosen default settings will prevent misuse by the inexperienced user. Additionally, the environment in which a user operates the system can have an impact on the functionality of that system.

2.7.4 Information Processing

Information processing principles of psychology directly apply to the design of a system. The three stage stages can be directly imitated in HiLs design.

The perception stage is scene as the communication delivery within the HiLs system. Here attention must be switched to from sources and maintained long enough for either the autonomous functionality of the system or a human operator to notice.

Much like information processing in human psychology, HiLs have a cognitive stage where communication processing takes place. Here, information gathered in the communication delivery phase is comprehended and the system gains knowledge it its environment.

Using the new knowledge from communication delivery and processing, the HiLs then applies this information in an action phase. Knowledge is retained and transferred to sub-components of the system.

For example, a robotic system may gain visual information through a camera sensor. This information is delivered to the proper processing sub-component. Perhaps the system is equipped with an image detection algorithm and searches for a certain object. Communication between this sub-component and others take places and actions are planned, eventually executed.

It is easy to imagine how a human would be in the loop of the cognitive and physical function- alities of a lower autonomy robot in the above situation. The operator may have to do the object identification and select the following actions.

But as autonomy increases, humans switch from performing low-level tasks to security-critical ones. A completely autonomous system would be able to perform all three stages of information

40 processing. However, even in highly autonomous systems, verification is necessary.

Examples of this are seen in military drone operations. But, when it comes down to “pulling the trigger” the human is required to do so, and will likely be in the future. For example, autonomous weapon bans are seeing success in the contemporary political climate. This can be seen as an indication that society is still hesitant on complete reliance on algorithms.

2.8 Philosophy

Philosophy serves as the foundation of reasoning and justification for objects and the world itself.

Through its methods and thought experiments, philosophy provides a thorough examination of an area of interest. Essentially, through philosophy we can have confidence that our understanding of the world is correct and not misguided. The following section will illuminate relevant areas and thought experiments to the proposed system.

The content of this thesis have analogues in philosophical thinking. Lessons from the first recorded philosophers, the Greeks, have exercised thought that remains relevant to future telep- resence dispatch systems. From Plato to the recent formalization of the simulation hypothesis, philosophy provides an additional viewpoint for an increasingly multidisciplinary problem set.

2.8.1 Plato’s Allegory of the Cave

The ancient Greek philosopher Plato, 429-347 B.C.E, holds ownership of several foundational theses.

He mused over the essence of objects, developing his theories of the Platonic Realm. Plato wrote using characters in dialogue, with Socrates being the protagonist. In the Republic, Socrates serves as our orator. One of the musings of Socrates is of the Allegory of the Cave. Socrates paints the scene of a group of humans who perceive the world in a simulated and falsified manner. These humans positioned at the bottom of cliff at the end of the cave, with no way of escaping. Additionally, they have been there since childhood so that the only thing they remember is being at the bottom of the cliff. Their captors provide ample food and water, but also project shadows on the end wall of the cave with the help of a bonfire. Free to enter the cave at will, the captors are able to see the world for what it truly is. Being rather dark at the bottom of the cliff, the trapped humans come to know reality as the shadows being presented for them on the wall. The captors are able to present different realities to imprisoned.

Immediately, one can see the connection between the perceived reality of the captive and the

41 simulated reality presented during a VR experience. Clearly, it is the case the those who participate in virtual reality are doing so willingly, thus remain free unlike the Plato’s cave inhabitants. However, lessons learned from metaphysical readings of Plato’s Allegory of the Cave transfer over into the domain of VR. For example, one is left to question if their own reality is any different than the cave dwellers. The virtual reality experience, with its clear analogue of the captor being the developer of the experience, posits similar concerns.

A reoccurring theme of Plato’s work is that the world as presented to humans is of a false or imperfect way. The perfect forms exist in a realm where they exist in a pure, unchanged manner.

Due to the limits of the human senses, we are left unable to perceive the pure forms. Even if we could, its likely we would lack the cognitive capacity to understand them. How similar to the experience of participating in VR.

2.8.2 Leibniz’s Possible Worlds

The seventeenth century German philosopher, Gottfried Willhelm Leibniz, heavily contributed to multiple academic fields. He is hailed as the last person able to hold the title “universal genius”.

The explosion of academic achievements to follow would render such a title impossible. Leibniz worked in mathematics, geology, law, history, geology, and the philosophical ares of logic, religion, metaphysics, and epistemology.

Modal philosophy, unlike the study of human sensory modalities, consists of analysis of questions pertaining to necessity, possibility, and contingency. Historically, philosophy has struggled to answer questions like “What would happen if Alan Turing never developed the concept of computability?”, where the answer’s correspondence to truth is not directly clear due to the relevant set of facts not have happened. Leibniz’s concept of possible worlds has given a tool set to handle such challenges.

The understanding of the world and modality can be partitioned into two camps: the possible worlds and the actual worlds. Leibniz means by possible world that a set of com-possible facts that could have been brought into existence if God had not created the actual world, which Leibniz characterized as the finite set of things that God had created due to his Goodness and perfection

[27]. Secularly speaking, the actual world is the set of facts that is the case and possible worlds are sets of facts that could have been the case if not for the actual world existing. Clearly, our experience points to us being in the actual world, then there must exist at least one possible world.

42 If each fact has its own counterfactual, then we can see how the alternative to what is actual is possible. Here we will leave further musings to works of philosophy, but Leibniz’s possible worlds provides an important metaphysics that can be used in dealing with virtual reality.

With the aid of virtual reality, developers are able to create possible worlds for user’s. Augmented reality exists in a sort of middle-ground between the actual and possible worlds, showing what could have been the case. Furthermore, the formalization of possible world semantics of Saul Kripke provide an actual way of using logic to discuss modality in a sufficient manner. With our virtual reality experiences expressible in modal logic, we can speak of truth and falsity what a user feels.

2.8.3 Descartes’ Dualism

Rene Descartes, the seventeenth century French mathematician-scientist-metaphysician, has a legacy in philosophy as large a Plato’s. His mathematical contributions, which surely have allowed for mod- ern day computing and robotic systems, are not his only contributions to the theme of this thesis.

It is Descartes metaphysics that provides us with a way of understanding the subjective virtual re- ality experience. His metaphysics, often introduced by his coining of the phrase “cogito, ergo sum”,

Latin heard around the world, “I think, therefore I am”, provided the foundation for subjective experience being the grounding factor for existence and truth. However, his meditations on human consciousness extended beyond the now commonplace utterance.

Descartes posited on the mind-body problem, which aims at understanding the underpinnings of consciousness. Are my thoughts a product of the physical processes in my brain or does my mind exist separate of my body? In his response to the mind-body problem, Descartes purports that our minds and body are distinct but not separate, in a doctrine know as dualism. Even though the support of dualism has waned over the years since Descartes first formalized it, we can use his concept to understand the experience of telepresence.

Phenomologically, we experience things much like the operator of virtual reality telepresence system should. Humans form intentions and then physically act them out. The telepresence operator does much of the same, forming higher-level goals and then executing them in smaller steps of a remote system.

43 2.8.4 Ethical Theory

Ethical theory provides a way of understanding the moral components of actions and decisions.

Normative ethics focuses on actors in the world and the formation of moral concepts [3]. Three main camps make up normative ethical theory:

1. Virtue Ethics

2. Deontology

3. Consequentialism

Together, these three subdisciplines attempt to solve the same set of question, albeit in different approaches. It is essential to have an understanding of normative ethics if one wishes to implement a system with autonomy. Neglect of the philosophical history of this would likely lead to improper execution in the world. Robot developers have a duty to utilize normative ethical theories in their thinking.

Virtue Ethics: Rather abstract in definition, Virtue Ethics views virtue as a foundational topic.

Virtue is the central feature, to obtain it requires practical wisdom and honest actions. The possessor of virtue is good, and virtue comes in degrees [23]. Agent-based virtue ethics views virtue as quality an agent possesses. An action is deemed good if done or would be performed by a virtuous person.

The robot developer ought to aim to be a virtuous person. Doing so would allow them to express this theory within in their work. One can see their actions as contributing to a larger whole, and it is their responsibility to steer the field in productive and helpful directions.

Deontological Ethics: Deontological ethics is often defined as being the rule-based ethics [3].

Instead of determining the moral ramifications of an action, deontology establishes guidelines for agents to follow that are permissible. An agent is duty bound to perform an action or not. A choice is is right if it confirms to some moral norm. One can think of programming a set of rules for a robot to follow that would lead to the permissible or required actions being performed. However, actually achieving this is difficult. Semantic facilities are required to contextualize a situation and extract relevant components. Such an attempt to formal this thinking has been done in the form of deontic logic [28]. This logic, however, is viewed often as purely theoretical and not complete.

Regardless, familiarity with deontological ethics provides a developer with a set of cognitive tools

44 to understand what kind of machines are permitted to be created and what actions those machines should make.

Consequentialism Consequentialism can best be understood when compared to deontological ethics. Where deontological ethics focuses on duty, consequentialism identifies the outcome of an action to be most important. It holds that an action is permissible if it maximizes the good [42]. The good, then, is often defined in terms of hedonism. When this hedonism is identified as the theory of the good, consequentialism then becomes utilitarianism, which holds that an action is permitted so far as it produces more happiness than the opposite. This can be confusing, as happiness is rather vague concept. Regardless, the emphasis on the consequences of an action is the important feature.

A developer can look at their work in context to the end result, and whether this result is desirable or not.

Theories Together: Each of the normative ethical theories attempts to solve the same ethical problem but through different means. No one theory is seen as superior when compared to others.

All three theories have their merits, and when used in conjunction one is able to access a moral dilemma in a sufficient manner. Simply relying on developers intuition of moral can likely lead to undesirable results. The understand of normative ethical theories may be able to mitigate future development and implementation problems.

2.9 Literature Conclusion

The above sections provide motivation and justification for the following proposed system and experimental design. It is essential to first have an understanding of the underlying principles used in the dispatch system. The experimental design takes heed of the existing literature and attempts to address a gap in situational awareness monitoring.

45 CHAPTER 3

Proposed System

Robots, through their sense-think-act cycle (Figure 4), are not much different than what creatures of biology do. This is why we find robots malleable to our needs, they can become surrogate actors.

Perhaps, that is where we ought to limit their functions, as malleable actors manifesting our needs.

One such need is that of ground transportation.

3.1 Proposed System

The proposed system is meant to be a use-case for how humans can remain in the loop of fully autonomous robots. A dispatch system design is presented that emphasizes a human operator’s situational awareness, and the responsibility of ensuing commands.

3.1.1 Motivation

It is expected that autonomous automobiles will be utilized on our roads in the coming years.

Predictions of when this will be achieved are varied, but all agree on this being an inevitability. As technology accelerates, allowing this future to be realized, society must come to terms with how these systems will be implemented.

If fully autonomous cars are possible, will we be comfortable with the on-board decision making algorithms having the final say on security critical systems? Highly autonomous systems, such as military drones, require a human-in-the-loop to “pull the trigger” or not. Even when the drone’s sensors have determined the target to be accurate, we do not allow it to make the final decision.

Similarly, there is an international push for a autonomous weapons ban, which would require humans to be in the decision making loop. Thus, society is already exhibiting hesitancy about augmenting human-like cognition.

These autonomous cars would be considered robots, and thus have principles of robotics apply to them. However, for most of their history, automobiles have been tools that aided humans in transportation. Without human action, these early automobiles would not work. Ignition of the

46 first cars required humans to hand crank the engine to start. Slowly, more and more features of automobiles became automated, see Section 2.2.4 for a discussion of the difference between automation and autonomy. Anti-Lock Breaking, cruise control and powered steering are examples.

This trend continued, with more parts of the sense-think-act cycle being handled by on-board computers. Newer models of cars now have automatic breaking and parking. Thus, by having all the necessary and sufficient components of robots (see Section 2.2.2), these new models are considered robots.

Thus, accepting semi-autonomous automobiles as being robots, developers must consider the impact of their use in society. Humans ought to remain in the loop as these systems continue to improve, eventually achieving full autonomy. The design of this proposed autonomous system emphasized humans as decision makers for security critical decisions. Situational awareness and its impact is highlighted as the primary factor for decision making. Furthermore, the ability for virtual reality technology to increase immersion is selected for the information presentation. This system respects autonomy of the human operators but assumes that the autonomous automobiles are sufficient for most navigation. Through a central dispatch node, responsibility is accepted and operator commands are selected to send to the fleet.

3.1.2 Architecture

The proposed system is composed of three separate components:

• Operators

• Dispatch System

• Fleet

Operators are responsible for being the humans in the loop for decision making. The Dispatch handles coordination, operator commands, the legal agency and assigning fleet requests. The fleet is a set of homogeneous autonomous automobiles that report environment information to dispatch.

Together, these three components make up a transportation system that allows for distributed autonomy and security-critical functionality.

47 Operator Component

The operator’s function is to provide commands to the dispatch node. Operators act individually, in isolation, but still are not single-handily responsible for the actions of the fleet. An operator may be the sole human supplier of commands to a single member of the fleet. Alternatively, if a

fleet member sends a high security-critical request to the dispatch, multiple human operators will be assigned to a single fleet member. Each operator acts as though the were the sole supplier of commands.

The operator is constantly monitored for psychological and physiological impairments. The health of the operator is highly stressed, for several ailments are found to be responsible for per- formance decrease. Virtual reality and telepresence-based sickness are frequent for user’s of that technology. Depending on the application, Post-Traumatic Stress Disorder and other stress disor- ders can result from humans making critical decisions. Anxiety and other performance factors are also relevant.

In addition to the above performance factors, maintaining situational awareness is an essential feature of this system. SA marks how cognisant a person is of an environment. Within the decision making procedure, confidence cannot be afforded without SA.

When an operator is required to “enter the loop” of a robotic system, there is a period of time before an adequate level of SA is achieved. Especially in the case of a security-critical decision request, this gap is a bottleneck which could have dire factors. Thus, it is important to figure out the obstacles to immersion and situational awareness.

Dispatch System

The dispatch component is the central node that handles fleet requests, assigns operators, and assumes responsibility for the actions of the fleet. Within the dispatch are planning, simulation, legal, and fusion engines. Planning is responsible for fleet disbursement, operator assignment, and route mapping. Simulation takes in fleet and environment data to predict future actions and supplies this information to the planning unit. The fusion takes in human commands and coordinates them with the planning unit. Aggregate commands are produced and sent to legal engines. The legal engine maintains responsibility of the system, and confirms commands.

The act of passing through a command requires approval that the command be feasible and

48 adequate. This approval acts as an acknowledgement of responsibility. For if it were not this way, direct communication between operators and the fleet would be the case, which we have seen as fallacious.

Fleet

The fleet is the set of autonomous vehicles on the road. They navigate the environment, and share status information with each other. Sensors pick up environmental information for both on- board planning and to provide to remote operators. Individual members of the fleet keep track of the confidence level of their planning algorithm When it drops below an acceptable threshold, a request is sent to the dispatch to assign an operator. Operators then have access to previous sensor information, allowing them to catch up to the vehicles situation. Once fully situationally aware, the operator passes commands to the dispatch, which the fleet must take if the confidence remains below the threshold. When the fleet member regains confidence, autonomy is regained and the operators may be assigned to another member of the fleet.

3.1.3 Trust Within the System

The proposed system described above handles information passing within the dispatch system.

However, actual implementation requires a user of the system. For the proposed design to be feasible, there must be a sufficient amount of trust held between the generalized system and the user. Each part of the this trust environment has a different amount of autonomy and responsibility.

This assumption works off of the paradigm that “trust leads to use” [37]. The relationship between trust and use has been studied within ergonomics and HRI. More familiarization with a system as result of prolonged use and experience can change one’s perspective of a system. A car owner knows the limitations of his or her vehicle than someone who has had no experience with that particular model. Additionally, trust has other factors within HRI. For example, transparency of how the system works impacts trust [18, 19].

Operators: The operators, being human, have full autonomy in that they can act freely. Resulting commands from the operator are only accepted in so far that they can be trusted. Not only are the operators fully autonomous, they also are the required component for security critical decisions. As discussed above, humans are prone to mistakes but are the only agents we trust to have the proper facilities to make these difficult decisions. The operators must have a high degree of trust placed

49 onto them in order to justify their placement within the system.

User: The user, while human like the operator, does not exhibit full autonomy in that they are not able to do otherwise. Users place requests to a dispatch and then voluntarily limit their autonomy by having something other than themselves make decisions about their wellbeing. The user of this emergent system is no different than the contemporary passenger of a taxi or Uber. Within the system, the user is only responsible for establishing high-level goals, such as destination and duration of travel.

Dispatch: The dispatch is an omniscient and omnipotent component that is responsible for the system itself. All actions can be traced back to the dispatch, from the initial user-request, to

fleet assignment and security-critical decision approval. The dispatch is fully responsible but semi- autonomous in that it does not supply commands of its own, relying on the humans or on-board digital operator to do so.

Fleet: The fleet is the least autonomous component of the system. A member is told where to go, how to get there, and can have its controls overridden. On-board computation is responsible for the majority of traversal, with requests sent to the dispatch when security-critical decisions are eminent.

In order for this system to function efficiently, with user’s wanting to use it and the mistakes mit- igated, trust must be abundant within it. Very similar to the information flow, different components are dependent on each other and can be represented via trust.

The user, being the purpose of the system, must feel confident in all components. Firstly, he or she must trust that the dispatch can efficiently get them to where there going. This involves a prompt and safe delivery. Furthermore, the user must trust the fleet member to have enough capabilities to do the majority, if not entirety, of the journey. Finally, the user must trust the human operator that their judgments are correct. The time it takes and the general safety are measures to predict the user’s trust in the system.

The operator must trust the information being presented to them is accurate. This involves low-latency data transmission from the fleet to the dispatch, which processes the data into infor- mation for the operator to utilize. Thus, the operator’s trust functions as pipeline from the fleet to themselves.

50 The dispatch must trust that the data from the fleet is sufficient. Confidence in sensor status as well as quality are marks of this trust. Additionally, the dispatch must have trust that its operator’s are in a position to provide insight not privy to the dispatch itself. Here is where the importance of SA comes in. SA serves as a confidence factor in how much trust can be given to an operator’s response.

The fleet then must have trust in its own sensors and in the commands received from the dispatch. For if this the latter breaks down, there would be no need to rely on a dispatch, all decision making could be done on-board the fleet.

3.2 Assumptions

Because this proposed system is not yet feasible, a set of assumptions surrounding technological progress have been made. This section will elicit those assumptions, along with reasons why they are seen to be possible.

Embedded World: With embedded systems frequently being integrated with nearly all conceiv- able applications, we are gaining access to an unprecedented amount of data. It is not far-fetched to imagine our traffic system, both pedestrian and automobile, to be also follow this trend. Safer and quicker routes can be planned in accordance to this new information. Since human behavior can be rather unpredictable, paths that do not cross heavily traversed areas can be made.

Traffic light patterns can be synchronized with large swaths of cars for smart traffic flow. The autonomous vehicles of tomorrow will be able to subscribe to such information as well as contribute their own. Fleets of vehicles will be able to communicate together and with the embedded world around them.

Human-In-The-Loop: It will be likely be the case that human will be hesitant to give up the freedom of driving on their own. There will be large groups of purists resistant to this change, similar to those who prefer manual shifting to the automatic clutch. The equally unpredictable driving behavior

Telepresence: Furthermore, this system also places assumptions on the advancement of telep- resence technology. On current restraint is the limited bandwidth, which limits quality and thus immersion. With 5G+ technologies, this is not seen as such a large issue. Additionally, it is expected

51 that virtual reality will become more and more life-like, with additional modalities being added.

Dispatch Features: The dispatch component makes a few assumptions about technology that is not yet available. To begin, the dispatch assumes that law will permit a centralized node taking responsibility and not either the human actors or robot manufacturers. Additionally, there does not exist an algorithm that takes the aggregate commands of multiple operators and produces one proper command. It is possible to do so, but at this time not much thought has been given to multiple robot operators to one robot.

52 CHAPTER 4

Materials and Methods

The proposed dispatch design serves as use case for telepresence mediated human-in-the-loop su- pervisory control. In order to claim that telepresence is effective in such a use, a small set of components must be verified. Namely, Situational Awareness has been identified as key factor in human-in-the-loop systems, but existing metrics are argued as being too subjective or based on memory.

This chapter expounds on the materials, methods, and design of the experiment to monitor SA.

A novel metric is introduced and limitations of experiment in comparison to the proposed design are explained.

4.1 Materials

The experimental design utilizes a simulated robot model, within a simulated environment, which is run on two separate computers.

4.1.1 Robot Model

The robot model used in these experiments are based on the Turtlebot 3 burger robot. Produced by

Robotis, Turtlebot is an educational platform for robotics research. Integrated with ROS and fully open-sourced, Turtlebot functions a foundation for implementation of newly developed features.

Specifically, the robot model used in the simulation is a replica of the existing Turtlebot model.

The robot model contain the following features:

• Differential Drive

• ROS Integration

• Video Streaming

• SLAM

53 Differential Drive: Differential drive is a two-wheeled method of driving in which each wheel operates independently. A passive castor wheel is placed at the back of the robot, and the two differential drive wheels are placed near the front. By independently controlling each wheel, precise navigation is possible, as well as 0-degree turning.

ROS Integration: ROS integration also makes the system highly customize-able over time, having access to a continuous stream of new capabilities provided by the open source community. Inte- gration of ROS makes for efficient message passing between components of the system. Different algorithms, such as planning, teleoperation control, and mapping run separately, communication via master node.

Video Streaming: Turtlebot3, by default, is equipped with a LIDAR system. While LIDAR is useful for several algorithmic packages, the data is on no use for an operator on its own. Instead, the model used for this experiment removes the LIDAR in exchange for an RGB-D camera. The camera model is Intel Realsense. It provides similar functionality of the LIDAR in that the camera produces a depth image, that can be converted to a laser scan used by mapping algorithms. Additionally, the camera serves a source of video stream, which can be easily comprehended by the operator, unlike the LIDAR output.

SLAM: SLAM, or Simultaneous Localization and Mapping, provide the robot with a way to navigate through space. A global map is constantly updated with local information the robot perceives. This map serves as a way for the robot to be spatial aware of its environment and provides a reference system for goals.

Simulation

Due to the nature of robotic materials being expensive, their use in dynamic environments, and the large amount of destruction possible by these systems, research in robotics relies heavily on simulation. Experimental systems, with their high degree of failure, can be safely verified in a simulated environment.

Gazebo is one of the common platforms for robot simulation, due to its integration with ROS, and it being open-sourced. It functions as physics simulator in which a developer can import their robot design and adjust parameters to mimic real-world situations.

54 rViz is the robot visualization suite available with ROS. All displayable messages can be seen here and are able to be formatted as seen fit. See

Figure 8 for an example. Using rviz, the visual camera feed and maps can be shown, however the participants will only see the video stream, see 4.1.3 for more. Figure 8: rViz Environment Simulation Models: The models used for the simulation portion of this experiment are modi-

fied from Turtlebot3 burger. As mentioned above, the LIDAR system equipped with the Turtlebot3 burger model is exchanged for an RGB-D camera, namely Intel Realsense.

The modified simulation model reflects this. The captured images from the camera in simulation are then passed via a ROS topic for any package to use. In fact, the resolution of the camera reflects the actual specifications of the physical one, with the same field of view and depth range.

Outside of the LIDAR/camera exchange, the simulation model does not differ from the physical model in any way. The same dimensions are used for body and wheel size. Physics surrounding wheel slippage and center of gravity are simulated identical to their real-world values. The simulation aims to be as close to real world as possible, but exact mimicry is not yet feasible. Still, simulations serve as a well-accepted paradigm to test robotic functionality in.

Simulation Environment: The simulation environment is a modified version of the open- source Willow Garage Officespace, as shown in

Figure 9. Robots roam around a predetermined path in four separate quadrants of the office.

Various objects are scattered around the floor of the office rooms for the users to find.

4.1.2 Software Figure 9: Gazebo Simulation of Willow Garage

The experimental system relies on a set of soft- ware components to function.

55 ROS Robotic Operating System, or ROS, has served as a foundation for countless robot applica- tions and research problems. Developed out of the need to solve problems in message passing, ROS functions as publish-subscribe model for transmitting data to the proper subsystems of a robot.

ROS is continuously maintained, with new long-term releases every two years, following the

LTS release pattern of Ubuntu and other Debian-based Linux systems. Currently, ROS Melodic is the de facto edition amongst developers, thus it is chosen to be the version implemented in this project.

Operating Systems: Two operating systems are used for this experiment, Ubuntu 18.04LTS and

Windows 10. Ubuntu is required to run the simulation and to use ROS for communication. The

ROS Core is hosted on Ubuntu, and also runs Gazebo, this simulation suite, and rviz, the visualizer.

Windows 10 is only used for the virtual reality applications, which require Steam VR and Unity.

Unity: The virtual reality application was designed using Unity. Unity is a popular game-development engine which is used to create small applications to full-scale video games. A background scene is created, which has all four camera feeds showing. From here, a user can select which stream they would like to view.

4.1.3 Hardware

The experimental design utilizes two computers that and virtual reality peripherals. Users only interact with the virtual reality display, but only to select the camera feed.

Computer

Two computers are used in order to run this experiment. A laptop a runs the simulation and ROS

Core, streaming data to the desktop computer, which handles the virtual reality application. The desktop also has a partition of Ubuntu, which is used to present the information in the traditional method.

Information Display

In order to present the simulation to the users, two different information displays are used. A user must be able to perceive the environment remotely, but immersion into the scene can differ.

Furthermore, the user is only shown video streams of the robots. There is no map visible, making users having to rely on their own spatial awareness abilities.

56 Traditional: The traditional method of information display of systems is through a monitor. Large amounts of human interaction with systems relies on monitors accurately displaying relevant information, being the de facto display.. This experiment uses a monitor split into four panels, each with a different video feed from the simulated robots. This is shown in Figure 10. During the single-robot sessions, one large video feed is shown that encompasses the entire screen.

Virtual Reality: Virtual reality allows for far Figure 10: Multiple Robot Monitor View more immersion into an environment than the traditional monitor method. Information is interacted with in a more personal way, often times in the first person view.

Unlike the monitor, virtual reality can re-

move background visual noise such as the desk

environment. Through a head-mounted display,

visual information is presented to the user. In-

dividual robot camera streams can be selected,

as well as multiple streams, as shown in Figure

11.

4.1.4 Experiment Setup

The experiment plans to examine factors sur-

rounding limitations of using virtual reality in

establishing Situational Awareness (SA). It is

motivated by the proposed system detailed in

the previous chapter.

Figure 11: Multiple Robot Virtual Reality View In anticipation of autonomous automobiles more consideration must be placed in respon-

sibility attribution, trust, and humans in the

57 loop.

At the top of the hierarchy of factors for systems that involve humans is situational awareness.

From SA all other considerations stem. There can be no confidence granted in a decision if the actor is not properly aware. Thus, it is important to understand how Situational Awareness can be maximized. As the proposed system hypothesizes, telepresence seems like the best way to permit a user to be situationally aware. That doesn’t prevent the actor from pursuing a goal ignorantly, but perhaps it ought.

The experiment, then, wishes to look into factors of SA when monitoring multiple robots, continuing a trend in HRI. By having the robots repeat paths with distinct checkpoints, we can establish the optimal amount of awareness a human holds by having them utter out the name and checkpoint type as a robot passes one. The time of these utterances are recorded and compared to the perfect score.

Additionally, changes to the robot environment will occur through the trial, unbeknownst to the subject. The operator is told to record the amounts of time each robot passes an area of interest, but are not told that some large change to the environment will occur. This takes form in shadows being turned off at a random point during the trial.

Pre-Test: A survey during before the trial will be filled out by the subject. This is to gather baselines on operator health and experience with the system. Through this the survey establishes:

• Current health of operator (Mental and Physical)

• Experience with VR

• Comfort Level

• Confidence

Demographics: In order to fully assess the data gathered, some preliminary demographic information is first required. Beyond the traditional sex, age, et cetera, it is important to gauge whether the participant has ever done VR before. For, we have seen some people are more prone to sickness or more adaptable, depending on differences in health. The following are the questions

58 included in the demographics survey:

1. Do you consent to your photo being taken during the in-trial session?

2. What is your age?

3. What is your sex? (Leave blank if you prefer not to answer)

4. What is your area of study?

5. What is your experience with VR?

Current Health: To keep the experiment as least invasive as possible, the health is assessed through a self-report survey, based on the Simulator Sickness Questionnaire (SSQ). While this experiment would technically provoke cybersickness and not Simulator Sickness, as discussed in section 2.4.4, the symptomatology is similar enough to allow for an overlap. Assessment of the this questionnaire is done similarly to SSQ, with three different categories measured in a range of degrees. The following table contains the survey.

VR Experience: The previous experience one has had with virtual reality can lend insight into how they will act when using it again.

People can build up immunity to different

sicknesses associated with virtual reality. Con-

versely, if someone has suffered from serious

symptoms in previous virtual reality experi-

ences, they can likely experience them again.

It is important to have an understanding

of this information when interpreting the re-

sults. For instance, if someone is prone to

sickness and does not get ill when using

this application, it can be understood as a

positive result of the quality of the experi-

Figure 12: Map of Simulation Environment ence.

59 Table 1: Symptoms Checklist for Health Survey

Symptom Not Present Present Disruptive

Body Discomfort O O O

Fatigue O O O

Sweating O O O

Stomach Awareness O O O

Nausea O O O

Increased Salvation O O O

Burping O O O

Heartbeat O O O

Headache O O O

Stress O O O

Blurred Vision O O O

Dizzy (eyes closed) O O O

Dizzy (eyes open) O O O

Vertigo O O O

In-Trial: The in-trial test is a self-reported ledger of encounters with areas of interest. There is a total number of checkpoints that the robot or robot fleet will pass. The goal of the subject is to remember the total amount of a single object. Since each robot will be moving autonomous and continuously passing marks, some areas are expected to be missed. There are four areas for the robots to navigate. Each area is split up into rooms and the robots will never cross each others paths. Participants are never show the map; each area appears unknown. Figure 12 shows the map of the simulation environment.

Additionally, the subject will be presented with an environmental change. This will occur quickly and will only be perceived by someone with a high degree of situational awareness. Control of the robot is not the purpose of the experiment, rather, passive monitoring is the aim.

60 Continuous Situational Awareness Monitoring (CSAM), as described above, is a novel situa- tional awareness method. Previous metrics, such as SART and SAGAT, have been criticized as being too subjective or too heavily reliant on operator memory[12]. These metrics either interrupt the trial or are taken after the fact. CSAM, on the other hand, does not suffer from these limitations for it does not rely on anything beyond a running total kept by the user and perception of objects.

Continuous Situational Awareness Monitoring is measured by report disparity. The final number of objects found by a participant is compared to the actual number of objects encountered. The difference between the two numbers is known as the report disparity. A negative report disparity is known as under-reporting, a positive report disparity is over-reporting, and a report disparity of

0 is a perfect score. See section 6 for more discussion on this metric.

Post-Trial: After the in-trial sessions, the participant is asked to take two more surveys. The post-trial health survey is meant to gauge any changes in operator’s health that resulted from the trial. It is the exact same survey as the pre-trial health survey.

Additionally, participants will take a post-trial recap survey. This survey contains a series of

Likert linear ranking questions, asking subjects to score immersion, resolution, mental workload, and comfort factor associated with both traditional and virtual reality interfaces. A score of 1 is low, and 7 would would be the best score possible. Along with the Likert questions, there are some extended response questions. The following questions are make up the Post-Trial Questionnaire:

1. How would you rank the immersion of the traditional/virtual reality approach? (1-7)

2. How would you rank the resolution of the traditional/virtual reality approach? (1-7)

3. What was the mental workload associated with the traditional/virtual reality approach? (1-7)

4. What was your comfort level like for the two traditional/virtual reality approach trials? (1-7)

5. Which system did you have more confidence in? (Short Answer)

6. What were the biggest contributions to success? (Extended Response)

7. What is the thing you dislike most about the system? (Extended Response)

8. What would you change about the experimental design? (Extended Response)

61 9. More features are planned on being added to this project, such as additional VR views and

head-motion tracking. Would you be willing to come back and test this system with those

improvements? (yes or no)

62 CHAPTER 5

Results

5.0.1 Demographics

The test population consisted of 10 computer science students at Kent State University, with current academic standings ranging from undergraduate to PhD. Of the participants, 9 were male and 1 was female. Academic level and sex were not taken into account when analyzing data.

With virtual reality being a relatively newer medium to for the average person to access, it is important to gauge previous use with VR for it can explain approach to the trials and resulting symptoms of the experience. The majority of participants were familiar with virtual reality, with only two never having used the medium before.

5.0.2 Pre-Trial Health Survey

The pre-trial health survey was focused on accessing the participants current symptoms. They were asked to categorize a list of symptoms as being 1) Not Present, 2) Present, and 3) Disruptive, as shown in Table 1. All participants scored low on current symptoms, with the mean symptom score being 1.1. The most common symptom was stress, with an average of 1.3. In general, the population was seen to be in good health with regards to current subjective symptom reporting.

5.0.3 In-Trial

The in-trial data gathered were self-reports of the participants. A report disparity of 0 would mean that the subject reported 100% of the objects. See Table 2 for results.

For the single robot monitor trials (SRT), the mean report disparity was -2. This indicates that, on average, a participant reported two fewer objects than were actually encountered.

For the single robot VR trials (SRV), the mean report disparity was -.5.

For the multiple robot monitor (MRT) trials , mean report disparity was -1.8, whereas for the virtual reality (MRV) trials saw a mean report disparity of .6. The positive report disparity is a result of participants over-reporting the number of objects found.

63 Table 2: Results of In-Trials

Report Disparity SRT SRV MRT MRV

Mean -2 -0.5 -1.8 0.6

Min (abs.val.) 0 0 1 0

Max (abs.val.) 4 5 5 4

The largest under-reporting of objects, i.e. negative mean report disparity, was -5 during a multiple robot traditional monitor trial.

The largest over-reporting was +7 during a multiple robot virtual reality trial. This is consistent with the trend of over-reporting being exclusive to the virtual reality trials, aside from one instance of over-reporting.

Virtual reality saw both under- and over-reporting, whereas the traditional monitor trials saw only under-reporting.

Overall, the virtual reality trials reported closer to perfect with .55, either over or under reported objects. Traditional monitor trials were an average of 1.9 objects away from a perfect score.

5.0.4 Post-Trial

Health Changes

Changes in health were accessed by having the participants fill out the modified SSQ again.

Compared to the Pre-trial health survey, participants were slightly more uncomfortable with present symptoms, scoring 1.2 post-trial versus 1.1 in the pre-trial. The largest reported symptom was fatigue, which had half of the participants reporting noticeable symptoms of. This is common for virtual reality studies.

Likert Questions

The post-trial survey had the participants rank the immersion, resolution, mental workload, and comfort level via a series of Likert questions from 1-7. See Table 3 for results.

When comparing the level of immersion between the traditional approach and virtual reality, the participants found them to be similar, with the traditional approach averaging 4.5 and virtual reality with 4.8. This indicates that virtual reality was more immersive than the traditional viewing method via monitors.

64 Table 3: Results of Post-Trial Likert Questions

Likert Question Traditional VR

Immersion 4.5 4.8

Resolution 6 3.9

Mental Workload 3.7 4.6

Comfort Level 6.2 4.1

The traditional method had better viewing resolution than the virtual reality displays, with the traditional scoring 6 and virtual reality only scoring 3.9.

Participants were also asked about the mental workload associated with both of the displays.

They found that it was harder to keep track of objects in virtual reality, which scored a 4.6 compared to the traditional method scoring 3.7.

Finally, subjects were asked to score their comfort level during the experiment. The results show that it was more comfortable using the traditional methods, which scored a 6.2, when compared to the virtual reality method, which scored 4.1.

These results fit with following question, which asked users ”which system did you have more confidence with?”, where the majority of users answered that the traditional method was more reliable.

Written Response

The post-trial survey additionally included extended response questions which addressed the strength, weakness, and possible changes to the design of the experiment.

In general, the responses were mixed, with some participants finding virtual reality to be the key to success, where others found the clarity of the picture in the traditional method to be more important. Additionally, speed of the robot was also found to be a contributor to success.

When asked what did they dislike the most, almost all participants indicated that some part of the virtual reality experience to be the main contributor. For example, many people mentioned the poor resolution and disorienting effect of virtual reality to hinder their performance.

Finally, when asked what would they like to change, participants mentioned several different aspects. Some would like more control, whether it be manual control of the robot or head-tracking

65 for the virtual reality portion. Additionally, multiple users mentioned that the resolution should be better for virtual reality. Interestingly, many participants also found the environment itself to be needing of change, for it was either too dark, boring, or confusing.

66 CHAPTER 6

Discussion

6.1 Discussion of Results

Analysis of the results indicate that telepresence mediated by virtual reality increase the situational awareness of monitoring remote environments through autonomous robots. This supported the hypothesis that underpins the dispatch system design. However, subjective responses would indicate that the traditionally monitoring methods be better, but this was not the case.

6.1.1 Pre-Trial

The pre-trial survey was aimed at gathering demographics about the participants. All subjects were computer science students, which may have impacted performance due to familiarity with the dis- cipline. Additionally, previous experience with virtual reality may have contributed to expectations of features such as resolution and head-tracking.

6.1.2 Health

It is important to gather a baseline of health symptoms when conducting virtual reality experiment.

When the trials were over, the participants were asked to fill out the same symptom questionnaire to determine the impact of virtual reality on health. As shown in results, there was no significant increase in any of the symptoms as result of the virtual reality sessions. This might be due to the fact that participants were asked to restrain their postural instability and sit with their head in a stationary position, in order to cut down on the risk of simulation sickness.

6.1.3 In-Trial

As described in section 5.0.3, participants were asked to keep track of one of five objects for each of the four runs. For example, the first run, one may be asked to count the number of hammers encountered, with the second round being asked to find cans, and so on.

To prevent participants from repeating the count of another participants, trials were held in

67 isolation with variance to what object was asked to be found. The participants score was then calculated by the difference between actual objects in the environment and objects found. This difference is called the mean report disparity.

On average, participants were closer to finding all of the objects when using virtual reality. As shown in the results, there were two different cases alternative to getting a perfect score, under- reporting and over-reporting. In addition to having to remember what the object being counted is, the actual count has to remain in memory. However small of a task this may be, it does place some mental pressure on the participants being the information processing required to identify an object.

Under-Reporting: If a participant got a mean report disparity of below zero, they under-reported the amount of objects encountered. Often times, participants reported that the resolution was not great, and the environment was dark. This could lead to objects being completely missed when moving throughout the environment. It is also possible that local environmental disturbances could have caused people to miss an object. Beyond being conducted in a populated research lab, unforeseen interruptions can occurs, such as one instance when the pop of a battery was heard.

Regardless, the traditional monitor trials saw exclusively under-reporting of objects. The reasons for this may be varied and dependent on the individual, with some scoring quite well. However, the meaning of under-reporting suggests that if this system was deployed, remote telepresent robot operators may be at a disadvantage when using traditional monitoring methods.

Under-reporting indicates that the situational awareness of the monitor is less than ideal, missing the perception of critical elements. In the case of this experiment, only one object had to be tracked.

Actual use of this system would have an operator monitoring several different objects in order to maintain proper situational awareness. Subsequent commands of an under-reporting operator would not have enough confidence granted to them.

Over-Reporting: In contrast to under-reporting, over-reporting is the result of a positive mean report disparity. Additionally, the explanation to over-reporting is more clear than under-reporting.

In both single robot and multiple robot segments, traditional and virtual reality methods saw over-reporting. When a robot navigates the environment, the localization algorithm, AMCL, will rotate in a circle to clear its costmaps and find the best path to the next waypoint. Sometimes,

68 this can confuse an operator, making them think that the object being shown is new, when it has actually been counted already. Furthermore, this trend is compounded in multiple robot trials, the operator tends to attend to one robot at a time. Due to the similarity of the environments, it becomes difficult to know if the room one robot is in has been visited already, even though no robot repeats its path. This is especially clear in the multiple robot virtual reality trials, which had a positive mean report disparity of +.6 for the entire population, the only such instance.

Over-reporting, similar to under-reporting, impacts the confidence of an operators commands and indicates low situational awareness. In contrast, however, over-reporting can actual show itself as being more cautious in an environment. If several security-critical factors are presenting them- selves to an operator, he or she may be more careful with the subsequent commands. However, this false caution can also make an operator less effective and efficient overall.

Virtual Reality Versus Traditional Monitoring

As shown in the results, virtual reality saw a lower mean report disparity (.55) than the traditional methods (1.9). These numbers are the absolute value of both over-reporting and under-reporting.

Thus, even through this primitive experiment, which restricted several operator inputs and infor- mation displays, virtual reality was more accurate of a medium for continuous situational awareness monitoring than the traditional monitor medium. Additionally, virtual reality was the only medium to see a positive mean report disparity, which is better than under-reporting.

6.1.4 Post-Trial

The results of the in-trial show that, even though virtual reality was found to be subjective inferior to the participants, it was more accurate overall. Virtual reality was more uncomfortable, and had more mental workload associated with it, but still provided participants with more situational awareness. This is also supported by the participants finding virtual reality to be more immersive, which lends itself to be more efficient in establishing and maintaining situational awareness.

6.2 Application of Results

The results lend credence to using virtual reality as a medium for keeping robot operators in the loop. Development of robotic systems ought to try to increase the immersion of the operator in order to maximize the ability for operators to make decisions. Virtual reality has been shown to be

69 more effective in continuous situational awareness monitoring.

6.3 Going Forward

This experiment was focused on human SA in low quality settings. The User Experience and

Interaction were not points of focus. Several different factors were mentioned in the post-survey and ought to be worked on in future studies:

• Control Schemes

• Sickness Prevention

• Environment Changes

• Resolution Improvements

Control Schemes: Participants indicated that having more control of the system would have been beneficial. However, this experiment was restricted in order to avoid testing operator control factors and instead was aimed at researching the difference between traditional monitoring and virtual reality for continuous situational awareness monitoring. Additionally, it was mentioned that having the ability to cycle through video feeds in the virtual reality trials led to an unfair advantage.

Eliminating that ability or adding additional similar functionality to the traditional trials would resolve this issue.

Sickness Prevention: In order to reduce the risk of participants experience simulation sickness, they were told to keep their posture still. Because the participants had no head-tracking or control of the robot, movement would likely lead to the experience of vection, as identified in preliminary testing. This led to the comfort of the virtual reality trials being impacted. Adding more function- ality to the virtual reality interface would improve the subjective response of the participants and may lead to better results.

Environment Changes: Throughout the trials, at randoms points, the shadows would be turned off, resulting in a slight change of the environment. The participants were asked if they noticed any changes after the session. Only in the virtual reality trials did they report a change. This was not quantifiable, but was meant to challenge the selective attention of participants. Adding a

70 repeatable, and more noticeable environment change would allow for these anecdotal response to be given scientific value.

Also, many participants suggested more variance and slight improvements to the environment would be appreciated. The monotone environment was specifically chosen to mimic the similarity of larger environments, where one street may appear the same as another. However, improving the simulation environment could allow for additional factors to be tested.

Resolution Improvements: All participants, either in the post-trial survey, or through verbal explanation during the trials, were disappointed with resolution of the virtual reality display. This is due to virtual reality system being a bit outdated. Implementation with a newer system would improve resolution. Additionally, an increase to bandwidth would allow for the images to be trans- mitted at a higher fidelity.

6.4 Future Work

This experiment serves a foundation for gauging the impact of additional functionality in continuous situational awareness monitoring. In conjunction with the previously discussed suggestions, there are several features that can be subsequently added and compared:

• Increased Immersion

• Physical Robot Testing

• Additional Information Presentation

Increased Immersion: The immersion factors of the experiment as is are quite limited. Only visual information is presented, and by adding more modalities, the degree of telepresence can be increased. Audio of the environment can provide an operator with more resources for making deci- sions. For instance, determining if a robot is stuck or turning in place would be easier. Ultimately, the aim is to make the user feel as though there were the robot.

Physical Robot Testing: The experiment only was run in a simulation environment, which allows for a repeatable and controllable scenario. However, having the same experiment run with actual robots may produce different results. Because the robot model in the simulation can be easily recreated as a physical model, this would be provide a nice comparison between situational awareness in a static environment versus a dynamic one.

71 Additional Information Presentation: As previously mentioned, participants were only pre- sented with the visual information from the robot’s cameras. Providing robot speed, orientation, and a dynamic map would allow for a users to be able to familiarize themselves with robot envi- ronment better. This would be more akin to how a system like this would actually be deployed, with all relevant factors being given to an operator.

6.5 Obstacles of Implementation

This thesis essential was split into two parts: the development of a use-case and an experiment on a single claim of the use-case. Namely, the use-case was the design of the telepresence dispatch system for human-in-the-loop robot control. The central claim of this use-case was that humans ought to remain in the loop of robot control, and virtual-reality mediated telepresence is the best way of doing so due to the increased immersion allowing for heightened situational awareness. This claim was then tested in an experiment, which compared the efficacy traditional monitoring and virtual- reality mediated monitoring of a remote environment on situational awareness. Above sections have discussed the limitations of the experimental design, and what can be done to improve it. Here, the obstacles to actual implementation of the use-case will be discussed. The following are the largest issues that are foreseen to exhibit some challenge to implementation:

• Latency

• Legal

• Smart-World

• Aggregate Commands

Latency: As previously mentioned, the latency involved in the transmission of sensor information is one of the largest issues currently facing telepresence systems. It is expected that this issue will be mitigated going forward as networking technology continues to improve. If bandwidth bottlenecks continue to occur, then it is unlikely that the proposed telepresence dispatch system can be achieved.

In order to fully immerse an operator into a remote environment, there cannot be noticeable latency.

Furthermore, it requires multiple sensor sources to achieve this level of immersion, which compounds the impact of bandwidth. More immersive experiences have yet to be created, so the issue of

72 bandwidth does not hold back progress yet. However, it remains important to solve this problem if we hope to have remote human operators of robots.

Legal: The law surrounding robotics still relies heavily on the function of robots being no different than machines. However, as more autonomy is added to these machines, they become increasingly independent agents in the world. There is no clear way to attribute responsibility to a non-biological agent. Punishment does not work the same for digital systems as it does for humans. Thus the inspiration for the central dispatch node in the proposed use-case. This node holds responsibility, and would be owned by some corporation. It is not clear why humans would want to be operators if they were held to be fully responsible when things go awry. Likewise, we cannot punish the car itself. So, some other individual must hold responsibility. Corporations fit this role in that they can be punished but individual people within the corporation are not as easily held liable. However, this assumption is based off of conjecture, and there is no real world case of how such a dispatch system could work. It is very likely that the evolution of law pertaining to robotics and autonomous agents will change going forward. As a society, we will need to come to some conclusion of the responsibility attribution problem involving robots if we wish to see them solve our problems and improve our livelihoods.

Smart-World: As previously mentioned, the proposed use-case relies on larges amounts of data to be available to the planning algorithms. Not only will the automobiles themselves be a source of environment knowledge through sensors, but so will the world itself. Embedded sensory systems can be placed throughout the environment that serve as additional sources of knowledge for things like pedestrian traffic flow and road conditions. Currently, we have not yet seen the large explosion of embedded systems or Internet-of-Things devices, but it is soon expected. This explosion is essential to the proposed use-case. Without it, the planning of the system and the information supplied to humans-in-the-loop will not be sufficient in making well-founded decisions.

Aggregate Commands: Finally, the last essential feature of the dispatch system that relies on yet undeveloped technology is the aggregate command algorithm. As described, the dispatch system takes in commands and sends them to a member of the fleet. It is foreseen that a single operator can send an incorrect command and lead to an undesirable result. To mitigate this, the proposed use-case allows for multiple-operator-single-robot scenarios. This was not tested in

73 the experimental design, which had a single operator and multiple robots to monitor. In order to produce a meaningful command to the fleet member, the dispatch must take an aggregate of the received operator commands. Such an algorithm does not yet exist in an sufficient capacity, but efforts ought to be made in the direction of multiple humans to single robot systems. The applications of this design extend far beyond a dispatch system and be can be used in several capacities.

6.6 Summary

The results of this experiment indicate that virtual reality is slightly better for continuous situ- ational awareness monitoring of robots in a simulated environment. The design was restricted in several ways so as to test this more specifically. Addition of more functionality and better hardware would allow for more factors to be tested. Results are reproducible, as the robots run a prede- termined route in a controlled environment. Ultimately, this experiments serves as foundation for understanding situational awareness and can be expanded upon for more fine-grained studies.

74 CHAPTER 7

Conclusion

This thesis has provided an argument for keeping humans in the loop of future autonomous robotic systems by using virtual reality mediated telepresence. The case for monitoring and gauging opera- tor situational awareness being the determining factor in operator command confidence is made. To support these claims, a dispatch system involving homogeneous autonomous transportation robots is proposed as a use case. A preliminary study involving SA is held, with accompanying surveys.

7.1 Literature Review

In order to establish validity for the proposed use case and experimental design, justification for terms and principles was held in the literature review chapter. Here, a thorough overview of

Robotics, Telepresence, Virtual Reality, Human-in-the-Loop, Human-Robot Interactions, Human

Factors, and philosophy demonstrated the reasons why the dispatch system was developed and what was important to test for in an experiment.

7.2 Design of Human-in-the-Loop Telepresent Dispatch System for Autonomous Robots

Motivated by the literature review and the trend of autonomous robotics, a dispatch system was designed that kept humans-in-the-loop for security-critical decisions. This system places opera- tor situational awareness as the most important factor in determining confidence in an operators instructions. Additionally, the design of the dispatch system address issues with responsibility attri- bution for when thing go awry. Multiple operators can send commands to the dispatch, which then takes a an aggregate of them and accepts responsibility for the ensuing commands. This removes the humans and the on-board robot planner as being responsible agents, and instead attributes re- sponsibility to the dispatch system, which would be own by some entity. The design of the system was mainly inspired by transportation robotics, such as autonomous vehicles, but can be applied to other robotic systems. Operators within the system are also seen to be telepresent, with their

75 situation awareness in the remote environment. This is achieved through the use of virtual reality as a medium.

7.3 Experiment

The design of the experiment was focused on testing situational awareness of robot operators. Two different mediums were used to achieve telepresence, a traditional monitor method and a virtual reality interface. Participants were asked to keep track of the number of times a specific object was encountered, in two different scenarios, single robot and multiple robot. This metric, Continuous

Situational Awareness Monitor, was motivated by the traditional situational awareness metrics,

SART and SAGAT, being either to subjective or too reliant on memory. The results show that virtual reality was more immersive and allowed for a better CSAM.

7.4 Future Work

This experiment lends itself to future studies on situational awareness, Human-Robot Interaction, and virtual reality studies. New functions, such as increased immersion and control schemes can be added. Additional participants can be brought in to further verify the results. CSAM can also be used to in physical robot implementations. There exist many incremental improvements necessary to achieve the proposed use-case design. Many of these are mentioned in 6, but others surely can be discovered that would promote more efficient implementations of the emerging technologies.

7.5 Summary

This thesis has introduced a design of an emergent robotic system, a new metric for situational awareness monitoring, as well as test suite for simulation experiments. However primitive the ex- periment design may be, it serves as foundation for more work to built upon due to its result in

finding Situational Awareness is improved by using more immersive medium such as virtual reality.

This work has provided an in-depth historical argument for why humans ought to remain in loop of autonomous systems, as well as provides a use-case for how this can be accomplished.

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