Robotics: Science and Systems XVII

Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics

Shen Li, Nadia Figueroa, Ankit Shah, Julie A. Shah


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.



    AUTHOR    = {Shen Li AND Nadia Figueroa AND Ankit Shah AND Julie A. Shah}, 
    TITLE     = {{Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics}}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2021}, 
    ADDRESS   = {Virtual}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2021.XVII.050}