Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
Trains perception-driven dexterous manipulation policies in simulation for zero-shot real-world transfer on humanoid robots. Focuses on bridging the sim-to-real gap for vision-based dexterous tasks.
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