Robotics: Science and Systems XVII

Provably Safe and Efficient Motion Planning with Uncertain Human Dynamics

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

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.

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Bibtex:

  
@INPROCEEDINGS{Li-RSS-21, 
    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} 
}