Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration

• URL: http://arxiv.org/abs/2103.04077v1
• Date: Sat, 6 Mar 2021 09:14:30 GMT
• Title: Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
• Authors: Xiaofeng Gao, Luyao Yuan, Tianmin Shu, Hongjing Lu, Song-Chun Zhu
• Abstract summary: We show 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.
• Score: 83.4081612443128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 (Reachability-Expressive Motion Planning), which jointly optimizes the physical cost and the expressiveness of robot motion to reveal the robot's motion capability 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.

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