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Nikolaidis, Stefanos; Hsu, David; Srinivasa, Siddhartha
The International journal of robotics research, 06/2017, Volume: 36, Issue: 5-7Journal Article
Adaptation is critical for effective team collaboration. This paper introduces a computational formalism for mutual adaptation between a robot and a human in collaborative tasks. We propose the Bounded-Memory Adaptation Model, which is a probabilistic finite-state controller that captures human adaptive behaviors under a bounded-memory assumption. We integrate the Bounded-Memory Adaptation Model into a probabilistic decision process, enabling the robot to guide adaptable participants towards a better way of completing the task. Human subject experiments suggest that the proposed formalism improves the effectiveness of human-robot teams in collaborative tasks, when compared with one-way adaptations of the robot to the human, while maintaining the human’s trust in the robot.
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