Show Me What You Can Do: Capability Calibration on Reachable Workspace for Human-Robot Collaboration
Gao, Xiaofeng, Yuan, Luyao, Shu, Tianmin, Lu, Hongjing, Zhu, Song-Chun
–arXiv.org Artificial Intelligence
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.
arXiv.org Artificial Intelligence
Mar-6-2021
- Country:
- Europe > United Kingdom
- England > Cambridgeshire > Cambridge (0.04)
- North America > United States
- Massachusetts > Middlesex County > Cambridge (0.04)
- Europe > United Kingdom
- Genre:
- Research Report (1.00)
- Technology: