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DiAReL: Reinforcement Learning with Disturbance Awareness for Robust Sim2Real Policy Transfer in Robot Control

arXiv — cs.LG•Monday, November 17, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
- The research introduces a novel disturbance-augmented Markov decision process (DAMDP) to tackle the challenges of sim2real transfer in robotic control, enhancing reinforcement learning methods. This development is significant as it aims to improve the efficiency and safety of robotic systems, which often struggle with sample inefficiency and resource limitations when trained directly in real environments. Although no related articles were identified, the focus on improving robustness and stabilization in control responses aligns with ongoing advancements in AI and robotics.
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