9 citations · 12 across the 9 of their papers we have counts for
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cs.RO2022★ 2 cited
Provably Safe Deep Reinforcement Learning for Robotic Manipulation in Human Environments
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 h…
cs.LG2022★ 9 cited
Provably Safe Reinforcement Learning: Conceptual Analysis, Survey, and Benchmarking
Hanna Krasowski, Jakob Thumm, Marlon Müller +3
Ensuring the safety of reinforcement learning (RL) algorithms is crucial to unlock their potential for many real-world tasks. However, vanilla RL and most safe RL approaches do not…