Human-Robot Mutual Adaptation in Shared Autonomy

Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own pr...

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Veröffentlicht in:2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI Jg. 2017; S. 294 - 302
Hauptverfasser: Nikolaidis, Stefanos, Zhu, Yu Xiang, Hsu, David, Srinivasa, Siddhartha
Format: Tagungsbericht Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 01.03.2017
Schriftenreihe:ACM Conferences
Schlagworte:
Adaptation models
Collaboration
Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic autonomy
Computer systems organization > Embedded and cyber-physical systems > Robotics > Robotic control
Computing methodologies > Artificial intelligence > Planning and scheduling > Planning under uncertainty
Computing methodologies > Artificial intelligence > Planning and scheduling > Robotic planning
History
Manipulators
Task analysis
Uncertainty
human-robot mutual adaptation
shared autonomy
planning under uncertainty
ISBN:9781450343367, 1450343368
ISSN:2167-2148
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Zusammenfassung:Shared autonomy integrates user input with robot autonomy in order to control a robot and help the user to complete a task. Our work aims to improve the performance of such a human-robot team: the robot tries to guide the human towards an effective strategy, sometimes against the human's own preference, while still retaining his trust. We achieve this through a principled human-robot mutual adaptation formalism. We integrate a bounded-memory adaptation model of the human into a partially observable stochastic decision model, which enables the robot to adapt to an adaptable human. When the human is adaptable, the robot guides the human towards a good strategy, maybe unknown to the human in advance. When the human is stubborn and not adaptable, the robot complies with the human's preference in order to retain their trust. In the shared autonomy setting, unlike many other common human-robot collaboration settings, only the robot actions can change the physical state of the world, and the human and robot goals are not fully observable. We address these challenges and show in a human subject experiment that the proposed mutual adaptation formalism improves human-robot team performance, while retaining a high level of user trust in the robot, compared to the common approach of having the robot strictly following participants' preference.
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ISBN:9781450343367
1450343368
ISSN:2167-2148
DOI:10.1145/2909824.3020252