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arXiv 2103.04077cs.ROcs.AI
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Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

UCLA Center for Vision, Cognition, Learning, and Autonomy (VCLA)(加州大学洛杉矶分校视觉、认知、学习与自主中心(VCLA));Massachusetts Institute of Technology(麻省理工学院);UCLA Department of Psychology(加州大学洛杉矶分校心理学系);Beijing Institute for General Artificial Intelligence (BIGAI)(北京通用人工智能研究院(BIGAI));Peking University(北京大学);Tsinghua University(清华大学)

Xiaofeng Gao, Luyao Yuan, Tianmin Shu, Hongjing Lu, Song-Chun Zhu

英文摘要:

Aligning humans' assessment of what a robot can do with its true capability is crucial for establishing a common ground between human and robot partners when they collaborate on a joint task. In this work, we propose an approach to calibrate humans' estimate of a robot's reachable workspace through a small number of demonstrations before collaboration. We develop a novel motion planning method, REMP, which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot's reachability to a human observer. Our experiments with human participants demonstrate that a short calibration using REMP can effectively bridge the gap between what a non-expert user thinks a robot can reach and the ground truth. We show that this calibration procedure not only results in better user perception, but also promotes more efficient human-robot collaborations in a subsequent joint task.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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