我了解ADEPT这个模型，它是用强化学习让机器人更灵活，学新动作时能直接用之前的经验，和没这么训练的方法比，效果更好。
ADEPT是一种强化学习框架，通过预训练和后训练方式提升多指机器人的灵巧性。该模型在23自由度的Kuka-Allegro和29自由度的Flexiv-Sharpa上测试，可从原始感知直接完成长时任务。它先针对通用物体摆放任务进行预训练，再将预训练行为作为先验用于下游任务的后训练。通过稳定后训练流程，实现模拟到现实的转移并达到人速级别的灵巧操作。
ADEPT: Accelerating Dexterity via Pre-Training and Post-Training using Reinforcement Learning
We introduce Accelerating Dexterity via Pre-Training (ADEPT), a large-scale reinforcement learning (RL) framework for learning sim-to-real transferable dexterity across high degree-of-freedom (DoF) robot embodiments that can solve long-horizon tasks directly from raw visuo-tactile perception. ADEPT pretrains a dexterous policy on a generic object reposing task, then post-trains downstream policies with this pretrained behavior as a prior. ADEPT enables learning new behaviors that are otherwise difficult to discover from scratch on multi-fingered robots and avoids learning the same set of skills over again for every new downstream task. The pretrained policy zero-shots the reposing phase of downstream tasks, but naïve RL fine-tuning rapidly degrades this capability during transfer. We address this with a stable post-training recipe combining behavior-cloning distillation, critic warm-up, and conservative on-policy updates. To safely exploit the full kinematic dexterity, we introduce a joint-space Geometric Fabric that mediates between the RL policy and the robot. We distill post-trained teachers into perceptive students that zero-shot sim-to-real transfer on two embodiments: a 23 DoF Kuka-Allegro with two RGB cameras, and a 29 DoF Flexiv-Sharpa with two RGB cameras and five vision-based tactile sensors, and can solve long-horizon tasks from challenging initial states with dexterity at human-level speed.