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Incorporating Contextual Knowledge Into Human-Robot Collaborative Task Execution

  • An essential measure of autonomy in service robots designed to assist humans is adaptivity to the various contexts of human-oriented tasks. These robots may have to frequently execute the same action, but subject to subtle variations in task parameters that determine optimal behaviour. Such actions are traditionally executed by robots using pre-determined, generic motions, but a better approach could utilize robot arm maneuverability to learn and execute different trajectories that work best in each context. In this project, we explore a robot skill acquisition procedure that allows incorporating contextual knowledge, adjusting executions according to context, and improvement through experience, as a step towards more adaptive service robots. We propose an apprenticeship learning approach to achieving context-aware action generalisation on the task of robot-to-human object hand-over. The procedure combines learning from demonstration, with which a robot learns to imitate a demonstrator’s execution of the task, and a reinforcement learning strategy, which enables subsequent experiential learning of contextualized policies, guided by information about context that is integrated into the learning process. By extending the initial, static hand-over policy to a contextually adaptive one, the robot derives and executes variants of the demonstrated action that most appropriately suit the current context. We use dynamic movement primitives (DMPs) as compact motion representations, and a model-based Contextual Relative Entropy Policy Search (C-REPS) algorithm for learning policies that can specify hand-over position, trajectory shape, and execution speed, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We demonstrate the algorithm’s ability to learn context-dependent hand-over positions, and new trajectories, guided by suitable reward functions, and show that the current DMP implementation limits learning context-dependent execution speeds. We additionally conduct a user study involving participants assuming different postures and receiving an object from the robot, which executes hand-overs by either exclusively imitating a demonstrated motion, or selecting hand-over positions based on learned contextual policies and adapting its motion accordingly. The results confirm the hypothesized improvements in the robot’s perceived behaviour when it is context-aware and adaptive, and provide useful insights that can inform future developments.

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Document Type:Report
Author:Ahmed Faisal Abdelrahman
Number of pages:x, 166
Supervisor:Paul G. Plöger, Alex Mitrevski
Publishing Institution:Hochschule Bonn-Rhein-Sieg
Contributing Corporation:Bonn-Aachen International Center for Information Technology (b-it)
Date of first publication:2020/03/03
Funding:We gratefully acknowledge the support by the b-it International Center for Information Technology.
Series (Volume):Technical Report / Hochschule Bonn-Rhein-Sieg University of Applied Sciences. Department of Computer Science (01-2020)
Keyword:Apprenticeship Learning; Domestic Robots; Human-Centered Robotics; Learning and Adaptive Systems
Departments, institutes and facilities:Fachbereich Informatik
Dewey Decimal Classification (DDC):0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2020/03/03
Licence (Multiple languages):License LogoIn Copyright - Educational Use Permitted (Urheberrechtsschutz - Nutzung zu Bildungszwecken erlaubt)