复旦等团队用REGRIND，只看一段演示就让机械手学会用剪刀和螺丝刀，零样本上手，动作和人一样自然。
REGRIND提出一种极简的重定向引导强化学习管线，仅需单个人类演示即可学习灵巧操作策略。该方法将人手与物体的运动重定向到机器人参考，保留空间和接触关系，并在仿真中训练残差RL策略以跟踪物体中心关键点。通过精细的系统识别，策略零样本迁移到真实硬件，在两个多指手上成功完成剪刀和螺丝刀等接触丰富的工具使用任务。实验系统性地分析了影响仿真到现实迁移的关键因素。
A Minimalist Retargeting-Guided Reinforcement Learning Recipe for Dexterous Manipulation
Recent work in humanoid whole-body control has found success with a simple recipe: retarget human motion to robot kinematic references, then train policies via reinforcement learning (RL) to track them. But how does this recipe transfer to dexterous manipulation? The answer is not obvious, as manipulation involves complex, contact-rich dynamics and requires delicate regulation of contact modes and forces. We present REGRIND, a minimalist retargeting-guided RL pipeline that learns dexterous manipulation policies from a single human demonstration. REGRIND retargets human hand-object motion to a robot reference that preserves hand-object spatial and contact relationships, trains a residual RL policy in simulation to track object-centric keypoints along that reference, and transfers the resulting policy zero-shot to hardware with careful system identification. The resulting policies produce fluid, human-like behavior on two different multi-fingered hands across contact-rich tool-use tasks, including operating a pair of scissors and turning a screwdriver. Through systematic hardware experiments, we identify and analyze the key factors that govern sim-to-real transfer in dexterous manipulation, offering practical guidance for retargeting-based learning in contact-rich settings. Videos and code are available at https://yunhaifeng.com/REGRIND.