Game-Theoretic Modeling of Human Adaptation in Human-Robot Collaboration

In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions,...

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Bibliographic Details
Published in:2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI pp. 323 - 331
Main Authors: Nikolaidis, Stefanos, Nath, Swaprava, Procaccia, Ariel D., Srinivasa, Siddhartha
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 06.03.2017
Series:ACM Conferences
Subjects:
Adaptation models
Collaboration
Computational modeling
Computing methodologies > Artificial intelligence > Planning and scheduling > Robotic planning
Force
Games
Human-centered computing > Human computer interaction (HCI) > HCI design and evaluation methods > User studies
Robots
Task analysis
Theory of computation > Theory and algorithms for application domains > Algorithmic game theory and mechanism design > Algorithmic game theory
human-robot collaboration
game-theory
human adaptation
ISBN:9781450343367, 1450343368
ISSN:2167-2148
Online Access:Get full text
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Summary:In human-robot teams, humans often start with an inaccurate model of the robot capabilities. As they interact with the robot, they infer the robot's capabilities and partially adapt to the robot, i.e., they might change their actions based on the observed outcomes and the robot's actions, without replicating the robot's policy. We present a game-theoretic model of human partial adaptation to the robot, where the human responds to the robot's actions by maximizing a reward function that changes stochastically over time, capturing the evolution of their expectations of the robot's capabilities. The robot can then use this model to decide optimally between taking actions that reveal its capabilities to the human and taking the best action given the information that the human currently has. We prove that under certain observability assumptions, the optimal policy can be computed efficiently. We demonstrate through a human subject experiment that the proposed model significantly improves human-robot team performance, compared to policies that assume complete adaptation of the human to the robot.
ISBN:9781450343367
1450343368
ISSN:2167-2148
DOI:10.1145/2909824.3020253