REGRIND Improves Dexterous RL Sample Efficiency via Human Trajectory Initialization
HaozhiQ · x · 2026-08-25
REGRIND introduces a simple approach to dexterous reinforcement learning. Instead of learning from scratch or using complex rewards, it starts from a calibrated human retargeting trajectory and lets RL refine it. The key idea is that good initialization combined with RL optimization outperforms learning everything from scratch, dramatically improving sample efficiency while preserving natural human-like motions.
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