| Title | Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics |
| Publication Type | Conference Proceedings |
| Year of Conference | 2021 |
| Authors | Li, S., N. Figueroa, A. Shah, and J. A. Shah |
| Conference Name | Robotics: Science and Systems (R:SS) |
| Date Published | 07/2021 |
| Abstract | Ensuring human safety without unnecessarily impacting task efficiency during human-robot interactive manipulation tasks is a critical challenge. In this work, we formally define human physical safety as collision avoidance or safe impact in the event of a collision. We developed a motion planner that theoretically guarantees safety, with a high probability, under the uncertainty in human dynamic models. Our two-pronged definition of safety is able to unlock the planner's potential in finding efficient plans even when collision avoidance is nearly impossible. The improved efficiency is empirically demonstrated in both a simulated goal-reaching domain and a real-world robot-assisted dressing domain. We provide a unified view of two approaches to safe human-robot interaction: human-aware motion planners that use predictive human models and reactive controllers that compliantly handle collisions. |
| URL | http://www.roboticsproceedings.org/rss17/p050.html |
