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Context-Aware Task Execution Using Apprenticeship Learning

  • An essential measure of autonomy in assistive service robots is adaptivity to the various contexts of human-oriented tasks, which are subject to subtle variations in task parameters that determine optimal behaviour. In this work, we propose an apprenticeship learning approach to achieving context-aware action generalization on the task of robot-to-human object hand-over. The procedure combines learning from demonstration and reinforcement learning: a robot first imitates a demonstrator’s execution of the task and then learns contextualized variants of the demonstrated action through experience. We use dynamic movement primitives as compact motion representations, and a model-based C-REPS algorithm for learning policies that can specify hand-over position, conditioned on context variables. Policies are learned using simulated task executions, before transferring them to the robot and evaluating emergent behaviours. We additionally conduct a user study involving participants assuming different postures and receiving an object from a robot, which executes hand-overs by either imitating a demonstrated motion, or adapting its motion to hand-over positions suggested by the learned policy. 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:Conference Object
Author:Ahmed Faisal Abdelrahman, Alex MitrevskiORCiD, Paul G. Plöger
Parent Title (English):Proceedings of the 2020 IEEE International Conference on Robotics and Automation (ICRA), 31 May - 31 August, 2020, Paris, France
First Page:1329
Last Page:1335
Date of first publication:2020/09/15
Abstract provided by the author.
Keyword: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 / 005 Computerprogrammierung, Programme, Daten
Entry in this database:2020/01/23