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Shen Li (MIT), Nadia Figueroa (MIT), Ankit Shah (MIT), Julie A. Shah (MIT) |
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| Paper #050 |
| Interactive Poster Session V | Interactive Poster Session VIII |
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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