Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids

• URL: http://arxiv.org/abs/2502.20396v2
• Date: Mon, 01 Sep 2025 23:08:08 GMT
• Title: Sim-to-Real Reinforcement Learning for Vision-Based Dexterous Manipulation on Humanoids
• Authors: Toru Lin, Kartik Sachdev, Linxi Fan, Jitendra Malik, Yuke Zhu,
• Abstract summary: We introduce a practical sim-to-real RL recipe that trains a humanoid robot to perform three dexterous manipulation tasks.<n>We demonstrate high success rates on unseen objects and robust, adaptive policy behaviors.
• Score: 56.892520712892804
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning generalizable robot manipulation policies, especially for complex multi-fingered humanoids, remains a significant challenge. Existing approaches primarily rely on extensive data collection and imitation learning, which are expensive, labor-intensive, and difficult to scale. Sim-to-real reinforcement learning (RL) offers a promising alternative, but has mostly succeeded in simpler state-based or single-hand setups. How to effectively extend this to vision-based, contact-rich bimanual manipulation tasks remains an open question. In this paper, we introduce a practical sim-to-real RL recipe that trains a humanoid robot to perform three challenging dexterous manipulation tasks: grasp-and-reach, box lift and bimanual handover. Our method features an automated real-to-sim tuning module, a generalized reward formulation based on contact and object goals, a divide-and-conquer policy distillation framework, and a hybrid object representation strategy with modality-specific augmentation. We demonstrate high success rates on unseen objects and robust, adaptive policy behaviors -- highlighting that vision-based dexterous manipulation via sim-to-real RL is not only viable, but also scalable and broadly applicable to real-world humanoid manipulation tasks.

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