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

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

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 <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">before</i> collaboration. We develop a novel motion planning method, reachability-expressive motion planning (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 align a non-expert user’s estimation of the robot’s reachability with its true capacity. 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. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup>