DSP框架：机器人从人类示范中自学灵巧操作
Dexterity from Supervised Play
教机器人学灵巧操作的论文：看人类玩几分钟，机器人自己采数据训练，数据量只要强化学习的1/490，成功率还不掉。
arXiv论文提出DSP（Dexterity from Supervised Play）框架，机器人通过行为克隆学习人类几分钟的操作演示，随后自主收集数小时交互数据训练世界模型。推理阶段由该策略为模型预测控制（MPC）提供候选动作序列，并通过世界模型内的self-play蒸馏出闭环目标条件策略。真实世界实验中，该方法在欠驱动推动和多指手中的任意姿态重定向任务上，成功率比hindsight行为克隆高69.6%，交互数据用量比强化学习少490倍仍达到相近成功率。整个流程无需任务专属示范，也不依赖sim-to-real迁移。
Dexterity from Supervised Play
How can robots autonomously collect the contact-rich experience needed to learn dexterous manipulation? We present DSP: Dexterity from supervised play, a framework in which a robot learns a play policy from minutes of human play through behavior cloning, then deploys it autonomously to collect hours of interaction data to train a world model. During inference, the same play policy then guides model-predictive control (MPC) by proposing candidate action sequences for evaluation under the learned world model. To accelerate inference, we also introduce a method for distilling a closed-loop, goal-conditioned policy through self-play within the learned world model. We evaluate this framework on underactuated pushing and any-to-any in-hand pose reorientation with a multifingered hand, in simulation and real world. In real world experiment, our method outperforms hindsight behavior cloning by 69.6% in success rate and achieves comparable success to reinforcement learning with 490 times less interaction data. These results demonstrate that supervised play can autonomously collect interaction data, guide model-based planning, and enable policy learning without any task-specific demonstrations or sim-to-real transfer.