Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
Jakob Thumm,Matthias Althoff,Jakob Thumm,Matthias Althoff
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...
