Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments. (arXiv:2205.06311v1 [cs.RO])

Deep reinforcement learning (RL) has shown promising results in the motion
planning of manipulators. However, no method guarantees the safety of highly
dynamic obstacles, such as humans, in RL-based manipulator control. This lack
of formal safety assurances prevents the application of RL for manipulators in
real-world human environments. Therefore, we propose a shielding mechanism that
ensures ISO-verified human safety while training and deploying RL algorithms on
manipulators. We utilize a fast reachability analysis of humans and
manipulators to guarantee that the manipulator comes to a complete stop before
a human is within its range. Our proposed method guarantees safety and
significantly improves the RL performance by preventing episode-ending
collisions. We demonstrate the performance of our proposed method in simulation
using human motion capture data.



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