全身灵巧性
Whole-Body Dexterity
AI总结：
本文提出全身灵巧性（WBD）框架，通过仿真到现实流程训练强化学习策略，使 Unitree G1 机器人实现全身操作，在真实任务中完成率达90%，远超对比方法。
摘要：
当前人形机器人的灵巧操作大多局限于末端执行器和简单抓取，无法像人类那样通过将 bulky 物体靠在手臂和躯干上支撑、滑动来调动整个身体。我们提出全身灵巧性（Whole-Body Dexterity, WBD），这是一种将机器人的身体和附属肢体作为接触表面来抓取和操作物体的通用框架。我们的仿真到现实（sim-to-real）流程首先生成可行的全身抓取数据集，然后通过从该数据集中采样初始和目标抓取，针对通用的任意到任意重定位目标训练强化学习专家，并将这些专家提炼为一个使用本体感觉和物体几何形状的单一策略。我们表明，提炼后的策略可零样本迁移到真实的 Unitree G1 机器人，该机器人可将篮球和纸箱重新定位到躯干周围区域，并完成将箱子夹在一只手臂下、从架子搬运到桌子并放置的长 horizon 任务。该控制器由人类远程操作员的粗略运动参考引导进行交互操作。在真实世界的全身操作任务中，提炼后的控制器完成 90% 的试验，而直接重定向和通用全身控制器的完成率分别为 18% 和 4%。
英文摘要：
Current humanoid dexterous manipulation remains largely confined to end-effectors and simple grasps, falling short of humans' ability to engage the entire body by bracing and sliding bulky objects against the arms and torso. We introduce Whole-Body Dexterity (WBD), a general framework that uses the robot's body and appendages as contact surfaces to grasp and manipulate objects. Our sim-to-real pipeline first generates a dataset of feasible whole-body grasps, then trains reinforcement-learning experts on a general any-to-any repositioning objective by sampling initial and goal grasps from the dataset, and distills these experts into a single policy that uses proprioception and object geometry. We show that the distilled policy transfers zero-shot to a real Unitree G1, where it repositions a basketball and a cardboard box to regions around the torso and completes a long-horizon task that stows a box under one arm, carries it from a shelf to a table, and places it. The controller operates interactively, steered by coarse motion references from a human teleoperator. Across real-world whole-body manipulation tasks, the distilled controller completes 90% of trials, compared with 18% for direct retargeting and 4% for a generalist whole-body controller.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
