DOCSLIB.ORG

Download PDF (66.6

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Download PDF (66.6 Index abnormally dangerous activities 38–9 behavioral reaction 17–18 academic expertise 112 drones 6 academic institutions/universities 222, legal protection for social robots 276 20–21, 214, 218, 219–20, 222, licensing 181–4 227, 231 accountability 267, 269, 376, 378, 382, movie robots 5 385, 386 anticybersquatting 17 Actroid F 189, 191, 192 appeals 249, 261, 264, 265, 266, 268 Advanced Medium-Range Air-to-Air Apple 191 Missiles (AMRAAMs) 55 Arduino 141 Aegis Combat System 53–4, 55 Arizona 271 agency capture 49 Arkin, R.C. 358 agriculture Asimov, Isaac 5, 26, 173, 357 tomato harvester 352–5, 358–9 Association for Computing Machinery Aibo robot dog 53, 55, 215, 218, 220, (ACM) 223 Code of Ethics and Professional Aiko 189, 191, 192 Conduct 146–7 aircraft Association for Unmanned Vehicle autopilot 26, 101 Systems International (AUVSI) 31 immunity: aviation industry 168 asymmetric element of modern warfare strict liability 364 abnormally dangerous activities asymmetry in power 49, 188, 210 38–9 Atlanta 244 Unmanned Aircraft System (U.A.S.) audit trails 265–6 38–9 augmentation, human 82 airports 10 Australia 244 Aldebaran 215 authenticity 220 Alston, P. 366 automobiles 19, 20, 31, 66, 275–6 Amazon 8, 108, 131, 132 antilock braking systems (ABS) 40, American Law Institute 29, 70 96 American National Standards Institute cruise control 26, 40, 41–2, 84, 258–9 (ANSI) 135 design defects 35 American Traffic Solutions 271 design and safety 26–7, 40, 90, 94, Anderson, C. 106 96 Anderson, K. 357, 381 driverless 12, 40, 55, 78–9, 180, 260 android fallacy 4, 18–21, 22 abnormally dangerous activities animal(s) 38–9 abuse 225, 226–8, 229 control 84–5 sniffer dogs 319–20 Google (GDC) 10–11, 12, 38, 55, anthropomorphism 18–21, 22, 175, 107, 108, 110, 111–12, 118, 189, 310–311, 386 275, 298 387 Ryan Calo, A. Michael Froomkin and Ian Kerr - 9781783476732 Downloaded from Elgar Online at 10/02/2021 06:45:24AM via free access 388 Robot law human-machine system and human-machine system and automation 95–101 automation 95–101 preemption 49 risk 85, 86 products liability 41–4 unreasonable risk 86–7 proposals for change 45–7, 49 speed 30, 90, 91–5, 100 electronic stability control (ESC) 38, civil disobedience 271 40, 96 experiment: coding traffic law see expert robots: delegation, coding the law relinquishment and law enforcement, automated responsibility 116, 118 235–6, 239, 245, 254, 257–9, driverless car 107, 108, 110, 260, 268, 271 111–12, 118, 120 necessity defense 268 GPS technology 15, 81, 235, 236, strict liability 242, 243, 244, 245, 254 abnormally dangerous activities errors 262–3 38–9 law enforcement and private texting-while-driving restrictions 90 sector devices 255 autonomy 53–6 malfunctions 257 consent 187, 202, 203, 205, 210 insurance 31, 43, 45–6, 180 degrees/levels of 56, 57–8, 74, 215 law enforcement, automated 250, increasing 10, 11, 18, 25, 41, 58, 260, 266–7 88–9, 174, 189 experiment: coding traffic law see low level or no 9 coding the law Wizard of Oz model 161 GPS technology 15, 81, 235, 236, environments 58–61 242, 243, 244, 245, 254, 255, ground-based teleoperated robots 8 257, 262–3 law enforcement, automated 260 legal integration of algorithms robot interrogators 319, 329 257–9 silence, right to 327, 329 license plate reader 246–7, 248–9 lethal autonomous weapons see necessity defense 268 killer robots red light cameras 247, 296, 298 machine IQ 58 social cost 271 mixed initiative or shared systems speed 235–6, 239, 245, 254, 257–9, 11, 21 260, 268 Mars Exploration Rovers 8, 53 tire pressure monitoring 248 over-ascription of 218 traffic cameras 244–5, 271, 293, Roomba 7, 9, 53, 55 299 self-taught programs 56–8 litigation 40 sexbots 187, 202, 203, 205, 210 negligence 62 silence, right to 327, 329 privacy by design 248 warehouse robots 8–9 robot language 78–9 see also driverless under automobiles control 84–5 human-machine system and bar codes 246 automation 95–101 Baraniuk, Richard 142 responsibility 88–9 bias 204 risk 85, 86 automation 264, 265, 266 safety and reasonableness 86–7 hindsight 77 speed 30, 90, 91–5, 100 law enforcement 252, 256, 264, 265, safety 266, 273, 287, 295, 296, 298, 303 design and 26–7, 40, 90, 94, 96 robot interrogators 329 Ryan Calo, A. Michael Froomkin and Ian Kerr - 9781783476732 Downloaded from Elgar Online at 10/02/2021 06:45:24AM via free access Index 389 biological weapons 342 children 219, 221, 223–4 biometrics 245, 246, 249–50, 271, autism 175, 222 293–4 child abuse 225, 230 blueprint test 36 China 333 borders 327 Christianity 189 border avatars 312–13, 315–16, 322, cinema 5–6, 7, 18, 194, 195–6, 207–8, 326, 328, 329 216–17 searches 322 Citron, D.K. 265–6, 267, 269 self-incrimination 323 civil disobedience 271 brain scans closed robots 166, 167–8, 184 electroencephalography 314, 315, closed systems 81 327–8 cluster munitions 377 functional magnetic resonance codes of conduct/ethics 136–7, 146–7, imaging (fMRI) 314–15, 316, 148, 181, 183–4, 185 325–6, 327–8, 329 coding the law 274–305 Breathalyzer test 325 background 275–8 Breazeal, C. 220 experiment: coding traffic law 275, Brooks, R.A. 157 278 burden of proof 42 instructions 304–5 Bush doctrine 359 experiment analysis 291–302 butterfly effect 82 certification 296 competition 297–9 California 61, 225, 352–3 expertise 295–6 Calo, R. 114, 164, 165, 168–70, 173, lawmaking authority 295–6 176, 180, 184, 239, 250, 267, 333 models insufficient at times Calvin, S. 334 296–7 Canada 244 new paradigms in punishment constitutional law 299–300 freedom of expression 211 problem areas 291–5 robot interrogator: right to silence second- and third-order effects 317, 323–6 299 robot interrogator searches social cost 300–302 317–22 experiment results 279–91, 292 interrogation 309 clustering 283–5 polygraph 314 concerns about creations 290–291 neuroethics 138 context 286–7 obscenity 198, 199–201 inter-ticket time 285–6 sexual violence 206 notice of speed changes 282 capabilities of robots 4, 7–11, 22, number of tickets 289–90 173–4 scalability 287–9 Čapek, Karel 5 sensor and timing error 282–3 Carpenter, J. 213, 217–18 subtle coding distinctions 288–9 cars see automobiles tolerance 279–82 causation 73, 82, 100 COGNIRON project 136 products liability cognitive fallacies 75 sophisticated robotic automobiles hindsight bias 77 42 Collins, Francis 139 chaos theory 289 Collins, Harry 109–10, 112 charities 10 common law tradition 62 chemical weapons 342 complex systems 73–4, 75–6, 81–2 Ryan Calo, A. Michael Froomkin and Ian Kerr - 9781783476732 Downloaded from Elgar Online at 10/02/2021 06:45:24AM via free access 390 Robot law Concealed Information or Guilty crimes with scienter requirements Knowledge Test 314–16, 318–19, 252, 293 322 necessity defense 268 confessions, coerced 324, 327 crowd-sourcing 143–4, 148, 160, 249, consent 294 data use 251 cruise missiles 7–8, 174, 336 interrogators 325, 326, 328 culinary consultant 105, 106 sexbots see separate entry cultural differences 146, 162 ubicomp (ubiquitous computing) custom, industry 30 349 customary international law 350, 368, constitutional law 12, 294, 329 372–3, 379, 386 Canada cyberlaw and robolaw 3–4, 12–15, 22 freedom of expression 211 cyborgs xii robot interrogator: right to silence 317, 323–6 da Vinci surgical robots 40–41 robot interrogator searches damages 317–22 statutory 170 First Amendment and freedom of Danielson, P. 143 speech 211, 268 Darling, K. 20 Fourth Amendment 252, 264, 268 data protection 348–9, 360 robot interrogator searches data-mining engines 108 317–19, 320–322 databases 75, 82, 221, 294 trespass/privacy 13–15, 17, 81, data aggregation 251 245 draft ethical robot license 180 Fifth Amendment: right to silence E-Z Pass 244 317, 323–6 identity information 245 Sixth Amendment: right to trial 264 motor vehicle records 246, 250, 294 consumer expectations test 35, 67, 70, open roboethics initiative (ORi) 147 71, 87 speed limits 245 consumer protection 12 death agency capture 49 wrongful 19 contract law 12, 61, 65 Defense Science Board (DSB) 95–6 liability and product safety 28, 32, defenses 28, 73, 168, 170 33 definitions doctrinal uncertainty 28, 29–31 artificial intelligence 12 merchantability warranty 32 automated law enforcement 239 open licensing 170 autonomous vehicle 12 control 83–5, 88–9 autonomy 55–6, 215 cookies 246 pornography 197, 198 Coordination Action for Robotics in robot xi–xii, 4, 5–7, 22, 151 Europe (CARE) 134 industrial 39 copyleft licenses 179 social 215, 228 copyright 294 surveillance 241 corporate risk management 86 dementia 222 corruption 269 Denmark 138, 244 cost–benefit test 33, 35, 36, 37, 42, 47, design 358 87 automated law enforcement 290, proposal for change 46 298 criminal justice system 264, 327 errors in design 241, 260–263 criminal law 61, 88, 203–4, 252 privacy by design 248 Ryan Calo, A. Michael Froomkin and Ian Kerr - 9781783476732 Downloaded from Elgar Online at 10/02/2021 06:45:24AM via free access Index 391 building code laws and building 277 eldercare 10, 41, 135–6, 175, 189, 219, closed and open robots 167 221 domestic robots 136 electroencephalography 314, 315, inequality: politics in 190–193 327–8 knowledge-sharing and informing electronic commerce 347, 348, 360 142–4, 145 elevator-riding robot moderating shared content open roboethics initiative (ORi) 150–151 131–62 privacy by 248 ELLEgirlBuddy 177–8 safety and 26–7, 40, 90–91, 94, 96 embodied machine intelligence and sharing and ethics implementation tort theory 51–77 140–141, 144–8, 161–2 autonomy and robots 53–6 elevator-riding robot 151–61 disembodied software 58–9 social robots 190 emergent behavior
Load more

Recommended publications
  • Evolving Symbolic Controllers
    Evolving Symbolic Controllers Nicolas Godzik1, Marc Schoenauer1, and Mich`ele Sebag2 1 Projet Fractales, INRIA Rocquencourt, France 2 LRI, Universit´eParis-Sud, France Published in G. Raidl et al., eds, Applications of Evolutionary Computing, pp 638-650, LNCS 2611, Springer Verlag, 2003. Abstract. The idea of symbolic controllers tries to bridge the gap be- tween the top-down manual design of the controller architecture, as advo- cated in Brooks’ subsumption architecture, and the bottom-up designer- free approach that is now standard within the Evolutionary Robotics community. The designer provides a set of elementary behavior, and evolution is given the goal of assembling them to solve complex tasks. Two experiments are presented, demonstrating the efficiency and show- ing the recursiveness of this approach. In particular, the sensitivity with respect to the proposed elementary behaviors, and the robustness w.r.t. generalization of the resulting controllers are studied in detail. 1 Introduction There are two main trends in autonomous robotics. There are two main trends in autonomous robotics. The first one, advocated by R. Brooks [2], is a human- specified deterministic approach: the tasks of the robot are manually decom- posed into a hierarchy of independent sub-tasks, resulting in the the so-called subsumption architecture. On the other hand, evolutionary robotics (see e.g. [13]), is generally viewed as a pure black-box approach: some controllers, mapping the sensors to the actua- tors, are optimized using the Darwinian paradigm of Evolutionary Computation; the programmer only designs the fitness function. However, the scaling issue remains critical for both approaches, though for arXiv:0705.1244v1 [cs.AI] 9 May 2007 different reasons.
    [Show full text]
  • A Model for Virtual Reconfigurable Modular Robots Thomas Breton, Yves Duthen
    A Model for Virtual Reconfigurable Modular Robots Thomas Breton, Yves Duthen To cite this version: Thomas Breton, Yves Duthen. A Model for Virtual Reconfigurable Modular Robots. 2011. hal- 01298411 HAL Id: hal-01298411 https://hal.archives-ouvertes.fr/hal-01298411 Preprint submitted on 5 Apr 2016 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. A Model for Virtual Reconfigurable Modular Robots Thomas Breton1 and Yves Duthen1 1VORTEX Research Team, IRIT UMR 5505 University of Toulouse, France [email protected], [email protected] Abstract and communication methods. We propose here a model to accomplish different tasks such as motion planning, This paper presents a model for virtual reconfigurable modu- object displacement, and structure reconfiguration; evolving lar robots in order to evolve artificial creatures, able of self- adaptation to the environment as well as good adjustment to a controller (a neural network) by the mean of a genetic various given tasks. For this purpose, a simulator has been algorithm. entirely developed with the assistance of a physics engine to represent force activities. One of the most crucial points It is true that any consideration taken in modular robotic in modular robot construction is the choice of module type, complexity and diversity.
    [Show full text]
  • Evolutionary Robotics in Two Decades: a Review
    Sadhan¯ a¯ Vol. 40, Part 4, June 2015, pp. 1169–1184. c Indian Academy of Sciences Evolutionary robotics in two decades: A review SAMEER GUPTA and EKTA SINGLA∗ School of Mechanical, Materials and Energy Engineering, IIT Ropar, Rupnagar 140001, India e-mail: [email protected]; [email protected] MS received 29 August 2014; revised 1 January 2015; accepted 11 February 2015 Abstract. Evolutionary robotics (ER) has emerged as a fast growing field in the last two decades and has earned the attention of a number of researchers. Principles of biological evolution are applied in the form of evolutionary techniques for solv- ing the complicated problems in the areas of robotic design and control. The diversity and the intensity of this growing field is presented in this paper through the contribu- tions made by several researchers in the categories of robot controller design, robot body design, co-evolution of body and brain and in transforming the evolved robots in physical reality. The paper discusses some of the recent achievements in each of these fields along with some expected applications which are likely to motivate the future research. For the quick reference of the readers, a digest of all the works is presented in the paper, spanning the years and the areas of the research contributions. Keywords. Evolutionary robotics; evolutionary control; robot morphology; body– brain design. 1. Introduction With the advent of bio-inspired computational techniques (Brooks 1986; Beer & Gallagher 1992; Haykin 1994; Ram et al 1994), the interests of many roboticists bend towards the utility of these techniques in the complicated designs and control of robots.
    [Show full text]
  • Evolving Robot Empathy Through the Generation of Artificial Pain in An
    Evolving Robot Empathy through the Generation of Artificial Pain in an Adaptive Self-Awareness Framework for Human-Robot Collaborative Tasks Muh Anshar Faculty of Engineering and Information Technology University of Technology Sydney This dissertation is submitted for the degree of Doctor of Philosophy March 2017 Bismillahirrahmanirrahim All Praise and Gratitude to the Almighty God, Allah SWT, for His Mercy and Guidance which have given me strength and tremendous support to maintain my motivation from the very beginning of my life journey and into the far future. I would like to dedicate this thesis to my love ones, my wife and my son, Nor Faizah & Abdurrahman Khalid Hafidz for always being beside me which has been a great and undeniable support throughout my study. CERTIFICATE OF ORIGINAL AUTHORSHIP This thesis is the result of a research candidature conducted jointly with another University as part of a collaborative Doctoral degree. I certify that the work in this thesis has not previously been submitted for a degree nor has it been submitted as part of requirements for a degree except as part of the collaborative doctoral degree and/or fully acknowledged within the text. I also certify that the thesis has been written by me. Any help that I have received in my research work and the preparation of the thesis itself has been acknowledged. In addition, I certify that all information sources and literature used are indicated in the thesis. Signature of Student: Date: 13 March 2017 Muh Anshar March 2017 Acknowledgements I would like to acknowledge and thank my Principal Supervisor, Professor Mary-Anne Williams for her great dedication, support and supervision throughout my PhD journey.
    [Show full text]
  • Valkyrie: NASA's First Bipedal Humanoid Robot
    Valkyrie: NASA's First Bipedal Humanoid Robot Nicolaus A. Radford, Philip Strawser, Kimberly Hambuchen, Joshua S. Mehling, William K. Verdeyen, Stuart Donnan, James Holley, Jairo Sanchez, Vienny Nguyen, Lyndon Bridgwater, Reginald Berka, Robert Ambrose∗ NASA Johnson Space Center Christopher McQuin NASA Jet Propulsion Lab John D. Yamokoski Stephen Hart, Raymond Guo Institute of Human Machine Cognition General Motors Adam Parsons, Brian Wightman, Paul Dinh, Barrett Ames, Charles Blakely, Courtney Edmonson, Brett Sommers, Rochelle Rea, Chad Tobler, Heather Bibby Oceaneering Space Systems Brice Howard, Lei Nui, Andrew Lee, David Chesney Robert Platt Jr. Michael Conover, Lily Truong Wyle Laboratories Northeastern University Jacobs Engineering Gwendolyn Johnson, Chien-Liang Fok, Eric Cousineau, Ryan Sinnet, Nicholas Paine, Luis Sentis Jordan Lack, Matthew Powell, University of Texas Austin Benjamin Morris, Aaron Ames Texas A&M University ∗Due to the large number of contributors toward this work, email addresses and physical addresses have been omitted. Please contact Kris Verdeyen from NASA-JSC at [email protected] with any inquiries. Abstract In December 2013, sixteen teams from around the world gathered at Homestead Speedway near Miami, FL to participate in the DARPA Robotics Challenge (DRC) Trials, an aggressive robotics competition, partly inspired by the aftermath of the Fukushima Daiichi reactor incident. While the focus of the DRC Trials is to advance robotics for use in austere and inhospitable environments, the objectives of the DRC are to progress the areas of supervised autonomy and mobile manipulation for everyday robotics. NASA's Johnson Space Center led a team comprised of numerous partners to develop Valkyrie, NASA's first bipedal humanoid robot.
    [Show full text]
  • Swarm Robotics Distributed Embodied Evolutionary Robotics
    Swarm Robotics Distributed Embodied Evolutionary Robotics (Evolutionary) Swarm Robotics: a gentle introduction Inaki˜ Fernandez´ Perez´ [email protected] www.loria.fr/˜fernandi ISAL Student Group ECAL 2017 September 8th 2017 Universite´ de Lorraine, LARSEN Team, Inria Nancy, France 1 / 10 Swarm Robotics Distributed Embodied Evolutionary Robotics So::: what is a robot swarm? Large/huge set of Focus in collective Real robots are cool::: simple agents dynamics but simulation works too! 2 / 10 Swarm Robotics Distributed Embodied Evolutionary Robotics So::: what is a robot swarm? Large/huge set of Focus in collective Real robots are cool::: simple agents dynamics but simulation works too! 2 / 10 Swarm Robotics Distributed Embodied Evolutionary Robotics Where to start::: many approaches By hand: ∼ engineering approach [Brambilla et al., 2013] Clones: classical EA with copies on each robot (homogeneous) [Tuci et al., 2008] Coevolution: either cooperative or competitive (heterogeneous) [Gomes et al., 2016] (distributed) Embodied Evolution runs onboard on each robot (heterogeneous) [Watson et al., 2002, Fernandez´ Perez´ et al., 2017] 3 / 10 Swarm Robotics Distributed Embodied Evolutionary Robotics What goals? ALife models to understand biology/test biological hypothesis ALife tools to build systems that can solve a problem for us 4 / 10 Swarm Robotics Distributed Embodied Evolutionary Robotics What robotic tasks? Navigation Flocking Item collection Foraging Shepherding ::: 5 / 10 Swarm Robotics Distributed Embodied Evolutionary Robotics
    [Show full text]
  • Simulating the Evolution of Soft and Rigid-Body Robots
    Simulating the Evolution of So and Rigid-Body Robots Sam Kriegman Collin Cappelle∗ Francesco Corucci MEC Labx MEC Lab e BioRobotics Institute University of Vermont University of Vermont Scuola Superiore Sant’Anna Burlington, VT, USA Burlington, VT, USA Pisa, Italy [email protected] [email protected] Anton Bernatskiy Nick Cheney Josh C. Bongard MEC Lab Creative Machines Lab MEC Lab University of Vermont Cornell University University of Vermont Burlington, VT, USA Ithaca, NY, USA Burlington, VT, USA ABSTRACT here two high-level Python wrappers around two dierent simula- In evolutionary robotics, evolutionary methods are used to optimize tors: Open Dynamics Engine (ODE) and Voxelyze. Our goal in this robots to dierent tasks. Because using physical robots is costly paper is to convey from experience what kinds of things are di- in terms of both time and money, simulated robots are generally cult/easy to instantiate in physics engines in general and how our used instead. Most physics engines are wrien in C++ which can user-friendly modules at least partially alleviate this, and how they be a barrier for new programmers. In this paper we present two may be extended in the future through open-source collaborations. Python wrappers, Pyrosim and Evosoro, around two well used simulators, Open Dynamics Engine (ODE) and Voxelyze/VoxCAD, 2 RIGID BODY ROBOTS which respectively handle rigid and so bodied simulation. Python Rigid body dynamics engines are generally what is most thought is an easier language to understand so more time can be spent of when one thinks of a simulator. ey are 3D engines where on developing the actual experiment instead of programming the every body in the simulation is ‘rigid’, meaning the body cannot simulator.
    [Show full text]
  • Proceedings First Annual Palo Alto Conference
    PROCEEDINGS OF THE FIRST ANNUAL PALO ALTO CONFERENCE An International Conference on the Mexican-American War and its Causes and Consequences with Participants from Mexico and the United States. Brownsville, Texas, May 6-9, 1993 Palo Alto Battlefield National Historic Site Southwest Region National Park Service I Cover Illustration: "Plan of the Country to the North East of the City of Matamoros, 1846" in Albert I C. Ramsey, trans., The Other Side: Or, Notes for the History of the War Between Mexico and the I United States (New York: John Wiley, 1850). 1i L9 37 PROCEEDINGS OF THE FIRST ANNUAL PALO ALTO CONFERENCE Edited by Aaron P. Mahr Yafiez National Park Service Palo Alto Battlefield National Historic Site P.O. Box 1832 Brownsville, Texas 78522 United States Department of the Interior 1994 In order to meet the challenges of the future, human understanding, cooperation, and respect must transcend aggression. We cannot learn from the future, we can only learn from the past and the present. I feel the proceedings of this conference illustrate that a step has been taken in the right direction. John E. Cook Regional Director Southwest Region National Park Service TABLE OF CONTENTS Introduction. A.N. Zavaleta vii General Mariano Arista at the Battle of Palo Alto, Texas, 1846: Military Realist or Failure? Joseph P. Sanchez 1 A Fanatical Patriot With Good Intentions: Reflections on the Activities of Valentin GOmez Farfas During the Mexican-American War. Pedro Santoni 19 El contexto mexicano: angulo desconocido de la guerra. Josefina Zoraida Vazquez 29 Could the Mexican-American War Have Been Avoided? Miguel Soto 35 Confederate Imperial Designs on Northwestern Mexico.
    [Show full text]
  • Evolutionary Robotics
    Evolutionary Robotics IAR Lecture 13 Barbara Webb Basic process Population of genomes, e.g. Decode each into robot binary strings, tree structures controller and/or morphology, e.g. weights in neural net, position of Produce new set of sensors genomes, e.g. breed, crossover, mutate Place in environment and run Use fitness to select for Evaluate behaviour reproduction, e.g. only if using a fitness function achieved task, or best e.g. achieve task, speed, individuals, or proportional time survived, find mate to fitness score Motivation • Lack of design methods that will ensure the right dynamics emerge from the environment-robot-task interaction • Automate the trial-and-error approach • Avoid preconceptions in design • Allow self-organising processes to discover novel and efficient solutions • Good enough for biology (and might help us understand biology) ‘Typical’ example Floreano & Mondada (1996): evolving Braitenberg-type control for a Khepera robot to move around maze • Eight IR sensor input units, feed-forward to two motor output units with recurrent connections • Standard sigmoidal ANN n 1 y f w x , where f (x) i ij j kx j 1 e • Genome – bit string encoding weight values • Fitness function: V(1 v)(1i) where i is highest IR value, V vleft vright v vleft vright • Population of 80, each tested for approx 30s • Copied proportional to fitness, then random paired single point crossover and mutation (prob.=0.2) • 100 generations, get smooth travel round maze Similar approach has been used to evolve controllers for more complex
    [Show full text]
  • Evolutionary Developmental Soft Robotics Towards Adaptive and Intelligent Machines Following Nature’S Approach to Design
    Evolutionary Developmental Soft Robotics Towards adaptive and intelligent machines following Nature’s approach to design Francesco Corucci, PhD November 16th, 2017 - ShanghAI Lectures Motivations: diversity, complexity, sophistication F. Corucci Evolutionary Developmental Soft Robotics 2 Motivations: intelligent and adaptive behavior Camouflage Creativity Skills Reasoning, cognition F. Corucci Evolutionary Developmental Soft Robotics 3 Motivations Can we automatically design a wealth of artificial systems that are as sophisticated, adaptive, robust, intelligent, for a wide variety of tasks and environments? F. Corucci Evolutionary Developmental Soft Robotics 4 Adaptivity, robustness, intelligence State of the art robots still lack many of these features Keep failing outside controlled environments (where they are most needed) DARPA Robotics Challenge Finals, 2015 F. Corucci Evolutionary Developmental Soft Robotics 5 Biologically inspired robotics (biorobotics) Cheetah robot, MIT Bat robot, Brown Soft fish, MIT OCTOPUS, SSSA RoboBees, Harvard Lampetra, SSSA Plantoid robot, IIT ECCE robot F. Corucci Evolutionary Developmental Soft Robotics 6 Biologically inspired robotics: Soft Robotics F. Corucci Evolutionary Developmental Soft Robotics 7 Biologically inspired robotics: pros and cons Pros: New technologies and design principles New knowledge related to the biological model (sometimes) Insights related to the intelligence of particular species (sometimes) Cons: Requires a lot of human knowledge and careful engineering Focuses on very specific
    [Show full text]
  • Biomimetic Bi-Pedal Humanoid: Design, Actuation, and Control Implementation with Focus on Robotic Legs
    Biomimetic Bi-Pedal Humanoid: Design, Actuation, and Control Implementation with Focus on Robotic Legs MICHAEL LOUIS OKYEN Thesis submitted to the faculty of the Virginia Polytechnic Institute and State University in partial fulfillment of the requirements for the degree of Master of Science In Mechanical Engineering Shashank Priya, Chair Steve Southward Alfred Wicks April 5, 2013 Blacksburg, VA Keywords: humanoid, biomimetic, legs, mechatronics, hydraulics Biomimetic Bi-Pedal Humanoid: Design, Actuation, and Control Implementation with Focus on Robotic Legs Michael Louis Okyen ABSTRACT The advancements made in technology over the past several decades have brought the field of humanoid robotics closer to integration into the everyday lives of humans. Despite these advances, the cost of these systems consistently remains high, thus limiting the environments in which these robots can be deployed. In this thesis, a pair of low-cost, bio-mimetic legs for a humanoid robot was developed with 12 degrees of freedom: three at the hip, one at the knee, and two at the ankle. Prior to developing the robot, a survey of the human-sized robotic legs released from 2006-2012 was conducted. The analysis included a summary of the key performance metrics and trends in series of human-sized robots. Recommendations were developed for future data reporting that will allow improved comparison of different prototypes. The design of the new robotic legs in this thesis utilized human anatomy data to devise performance parameters and select actuators. The developed system was able to achieve comparable ROM, size, weight, and torque to a six-foot tall human. Position and zero-moment point sensors were integrated for use in balancing, and a control architecture was developed.
    [Show full text]
  • DART: Diversity-Enhanced Autonomy in Robot Teams
    DART: Diversity-enhanced Autonomy in Robot Teams Nora Ayanian Abstract This paper defines the research area of Diversity-enhanced Autonomy in Robot Teams (DART), a novel paradigm for the creation and design of policies for multi-robot coordination. While current approaches to multi-robot coordination have been successful in structured, well understood environments, they have not been successful in unstructured, uncertain environments, such as disaster response. The reason for this is not due to limitations in robot hardware, which has advanced significantly in the past decade, but in how multi-robot problems are solved. Even with significant advances in the field of multi-robot systems, the same problem- solving paradigm has remained: assumptions are made to simplify the problem, and a solution is optimized for those assumptions and deployed to the entire team. This results in brittle solutions that prove incapable if the original assumptions are inval- idated. This paper introduces a new multi-robot problem-solving paradigm which relies on a diverse set of control policies that work together synergistically to make multi-robot systems more resilient in unstructured and uncertain environments. 1 Introduction The field of multi-robot systems (MRS) is growing at a rapid pace. Research in MRS spans many different areas, including automated delivery [1–3], surveillance [4], and disaster response [5, 6]. There have also been many successful demonstrations of increasing numbers of robots [7–9, 11–13]. MRS have also been successfully deployed in the field including in warehousing [15], manufacturing [16], and enter- tainment [17]. While these outcomes show the promise of MRS, the environments in which MRS have been successful are highly controlled, and some are highly in- strumented, enabling precise tuning of controllers and nearly perfect knowledge of environmental conditions.
    [Show full text]