共有自律における人間とロボットの相互適応
Human-Robot Mutual Adaptation in Shared Autonomy
人間の適応モデルを部分観測確率決定モデルに組み込み、人間が柔軟なら良い戦略へ導き、頑固なら信頼維持のため好みに従う相互適応手法を提案し、被験者実験で性能と信頼の向上を示した。
著者: Stefanos Nikolaidis, Yu Xiang Zhu, David Hsu, Siddhartha Srinivasa
分類: cs.RO
原文アブストラクト
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.
