Author

Sebastian Albrecht

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Preprint Sep 2026

Res-HIL: Human-Guided Residual Reinforcement Learning for Sample-Efficient Dexterous Manipulation

Res-HIL is introduced, a human-in-the-loop residual reinforcement learning framework that learns corrective actions on top of a frozen imitation policy that improves its pretrained base policies and outperforms imitation policies trained with five times more demonstrations.

M. Iavorskaia, C. Dietz, Sebastian Albrecht et al. · 0 citations