Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics

Shen Li, Nadia Figueroa, Ankit Shah, Julie Shah
2021 Robotics: Science and Systems XVII   unpublished
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
more » ... icient 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.
doi:10.15607/rss.2021.xvii.050 fatcat:qlzuf2dhojcyfm3c5asdzjb5nu