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Deliverable D4.3: Navigation Demonstrator

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Deliverable D4.3: Navigation Demonstrator TERESA - 611153 - FP7/2013-2016 Deliverable D4.3: Navigation Demonstrator Project Acronym: TERESA Project Full Title: Telepresence Reinforcement-Learning Social Agent Grant Agreement no. 611153 Due date: M36: November 2016 Delivery: November 30, 2016 Lead partner: UPO Dissemination level: Public Status: Submitted Version: v2.0 DOCUMENT INFO Date and Version Number Author Comments 01.11.2016 v0.1 Luis Merino Scheme 10.11.2016 v0.2 Noe Perez Macro actions and social nav- igation 14.11.2016 v0.3 Rafael Ramon Approach people section 22.11.2016 v0.4 Jesus Capitan Walking side by side section 25.11.2016 v1.0 UPO team First draft 28.11.2016 v1.1 Joao Messias Revision 30.11.2016 v2.0 UPO team Submitted Contents 1 Contributors . .7 2 Executive summary . .8 3 TERESA human-aware navigation stack . .9 3.1 Introduction . .9 3.2 Robot sensors for navigation . 10 3.3 The navigation stack architecture . 11 3.3.1 Behavior manager . 12 3.3.2 Architecture of navigation behaviors . 13 4 Social waypoint navigation . 16 4.1 Introduction . 16 4.2 Path planning . 16 4.3 Low-level control . 17 4.4 Learning social navigation . 17 4.4.1 Learning a RRT* cost function . 18 4.4.2 Features for social navigation . 21 4.4.3 Experimental results . 23 4.5 Navigation evaluation . 25 4.5.1 Benchmarking according to ERL-SR . 25 4.5.2 Social evaluation . 27 5 Yield........................................... 31 6 Approaching people . 33 6.1 Introduction . 33 6.2 GMMs for interaction modeling . 34 6.3 The reproduction planner . 35 6.4 Experimental results . 35 6.4.1 Gathering the data for learning . 36 6.4.2 Metrics . 36 6.4.3 Results . 37 7 Walking side-by-side . 40 7.1 Introduction . 40 7.2 Preliminaries . 41 7.2.1 Social force model . 41 7.2.2 Partially observable Monte-Carlo planning . 43 7.3 Hierarchical planner for walking side-by-side with a person . 45 7.4 Low-level controller based on SFM . 46 7.5 High-level planner based on POMCP . 47 7.5.1 States . 47 7.5.2 Actions . 48 7.5.3 Observations . 48 7.5.4 Reward function . 48 7.5.5 Transition and observation probability functions . 48 2 TERESA - 611153 7.5.6 Other issues . 49 7.6 Experiments . 50 8 Integrated Experiments . 54 8.1 Centre Sportif de l’Aube . 54 8.2 Les Arcades . 55 9 Conclusions . 56 D4.3: Navigation Demonstrator Page 3 of 62 List of Figures 1 The final version of the TERESA robot navigating among people . .9 2 Final disposition of sensors on TERESA . 11 3 SMACH viewer representation of the human-aware navigation macro-actions . 13 4 Finite state machine for navigation . 14 5 General architecture for navigation macro-actions . 14 6 Descriptive capture of the navigation planning architecture . 17 7 General cost function learning scheme . 20 8 Features for social navigation . 22 9 Social cost as Gaussian mixture function . 22 10 Scenarios for learning cross-validation . 24 11 Evolution of the parameters during learning . 24 12 Visual comparison of demonstrations and learnt paths . 24 13 Relative errors of the learnt paths . 25 14 Scenarios for navigation evaluation . 26 15 Scenario and Waypoints for navigation functionality evaluation . 26 16 Capture of the real experiments for social navigation evaluation . 29 17 Static scenario for social navigation evaluation . 30 18 Path of the dynamic scenario for social navigation evaluation . 32 19 Pre-defined yield behavior in doorways . 33 20 Demonstrated trajectories for the approaching task. 34 21 A view of the general set up performed to collect the data. 36 22 Costs evolution of GMM-RRT* approaches . 37 23 Planning paths vs. demonstrated paths . 38 24 Managing homotopies. 38 25 Generalization. 39 26 Social Force Model . 42 27 Walking side-by-side simulated environment . 50 28 Simulated results for the walking side-by-side behavior (I) . 51 29 Simulated results for the walking side-by-side behavior (II) . 52 30 Navigation experiments at Centre Sportif . 54 31 Pictures of experiment 4 at Les Arcades . 55 32 Approaching an interaction target . 56 33 Navigation to waypoint . 57 4 List of Tables 1 Rockin evaluation metrics for each waypoint . 27 2 Rockin evaluation metrics . 27 3 Results for social navigation evaluation with a static scenario with two people . 31 4 Results for social navigation evaluation with a static group of two people . 31 5 Results for social navigation evaluation of a free run with two people moving in the scenario. 32 6 Trajectory quality approaching a person. Smaller values are better for all metrics. The best values are highlighted in boldface. 38 7 Parameters for the walking side-by-side algorithms . 51 5 List of Algorithms 1 RRT*-IRL . 21 2 SFM-based controller for robot navigation . 46 6 TERESA - 611153 1 Contributors The authors of the deliverable are the members of the UPO team. Noé Pérez has written mainly Sections 4 and 5 and contributed to Section 3. Rafael Ramón has delivered Section 6. Jesús Capitán and Ignacio Pérez have mainly written Section 7. Luis Merino has written the introduction and conclusions and part of Section 3, and contributed, with Jesús Capitán, to the rest of the deliverable. The deliverable has been reviewed by Joao Messias of UvA. D4.3: Navigation Demonstrator Page 7 of 62 TERESA - 611153 2 Executive summary This deliverable is the outcome of Tasks 4.2 and 4.3 of the project. The demostrator itself is the navigation stack that has been deployed on the TERESA robot, and that has been tested, at least partially, in the experiments of the project, and in several local experiments. However, as there is no other document describing the details of some parts of the navigation stack, this document will not only show some results of the demonstrator, but it will also present the main techniques behind the navigation modules. With Deliverables D4.1 [9] and D4.2 [10], it completes the description of the main elements developed in WP4. The document first states in Section 3 the objectives of the navigation stack, moving from a classical navigation module to a human-aware navigation module, in which the presence of persons in the environment is considered at the different levels of the system. As it is not possible to devise a navigation module able to deal with all potential social situations, a set of navigation macro-actions are considered, and implemented, coordinated by a high-level behavior manager, that also integrate them with the body-pose macros described in [12]. Then, the particularities of the different macros are presented, and evaluated in local experi- ments. The human-aware navigation macro is described in Section 4. There, the main aspects of the motion planning and control elements are described, and how cost functions, learned from data, are considered to incorporate preferences related to normative social behavior. Section 6 describes the aspects of the approaching persons macro. There, it is shown how social constraints related to this task can be also extracted from data and incorporated into the previous motion planners. Section 7 describes a walking side-by-side macro, in which the robot should accompany one person towards a destination. First, a control-based approach considering a social force model is described. However, this task is a joint task between the person and the robot, in which the intentions and commitment of the person is not observable; also, this macro is particularly affected by uncertainties due to errors on the perception. Thus, the macro is implemented using a planner considering uncertainties to take into account these issues. Finally, Section 8 shows some results of the navigation stack integrated with the rest of the TERESA system in the Experiments at Les Arcades, Troyes. In these experiments, the per- ception modules developed in WP2, the HRI in WP3, the body-pose control of WP5 and the navigation of WP4 work together to provide a semi-autonomous telepresence system with so- cial intelligence. D4.3: Navigation Demonstrator Page 8 of 62 TERESA - 611153 Figure 1: The final version of the TERESA robot navigating among people. 3 TERESA human-aware navigation stack 3.1 Introduction Telepresence systems allow a human controller (the visitor) to interact remotely with people. Called by some "Skype on a stick", in such systems the visitor pilots a remotely located robot that results in greater physical presence than with standard teleconferencing. One of the po- tential problems of telepresence systems is the cognitive overload that arises by having to take, at the same time, navigation and interaction decisions. This may lead to mistakes at navigation and to pay less attention to the interaction and communication tasks [60]. Actually, partial autonomy in terms of navigation is a feature requested by telepresence users accord- ing to studies like in [21] and the findings of Deliverable D6.1 [15] on the user studies for the requirements of TERESA. In TERESA, the objective is to augment the social intelligence and autonomy of the telep- resence robot, so that the robot can execute the low-level navigation tasks and the user can concentrate in the interaction with his/her peers. Thus, the original Giraff telepresence robot has been upgraded throughout the project with new sensors for autonomous navigation, as described in Deliverables D6.3, 6.4 and 6.5 [16, 17, 18]. We will denote this new system the TERESA robot (see Fig. 1). Once the robot has the proper sensors, it is possible to deploy an autonomous.
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