发表机构
University of California, Berkeley（加州大学伯克利分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Workhorse从人类演示学习全身移动操作，通过视觉规划器预测五连杆目标并强化学习跟踪，在真实和模拟人形机器人上实现鲁棒的箱子分类等任务。
AI 中文摘要
人形机器人在仅依赖以自我为中心的RGB视觉和本体感觉来规划接触丰富的全身操作任务方面仍面临挑战。Workhorse从无机器人的人类演示中学习此类操作。一个视觉规划器预测五个连杆目标：躯干、双腕和双足的位姿。一个强化学习全身跟踪器在机器人上跟踪这些目标。两个策略分别在相同记录的人类位姿上独立训练，无需重新定向。我们增强每个策略的训练数据，以模仿另一个策略在部署时产生的误差。在真实的Unitree G1上，Workhorse用手和踢腿动作对箱子进行分类，接住抛来的箱子，并推倒和攀爬行李箱。在箱子分类过程中，我们展示了在有人推机器人或拿走箱子后的恢复能力。在演示房间的模拟副本中，系统在77%的回合中完成箱子分类，在40 N.s推力下完成率为64%。当两个策略从相同演示重新训练后，模拟的第二个人形机器人在无推力情况下完成箱子分类的回合比例为83%。
英文摘要
Humanoid robots still struggle to plan contact-rich whole-body manipulation from egocentric RGB and proprioception. Workhorse learns such manipulation from robot-free human demonstrations. A visual planner predicts five-link targets: the poses of the torso, both wrists, and both feet. A reinforcement-learning whole-body tracker follows them on the robot. Both policies train separately on the same recorded human poses, without retargeting. We augment the training data of each policy to imitate the errors that the other makes at deployment. On a real Unitree G1, Workhorse sorts boxes with its hands and a kick, catches a thrown box, and topples and climbs a suitcase. During box sorting, we show recoveries after a person pushes the robot or takes the box away. In a simulated copy of the demonstration room, the system completes box sorting in 77% of episodes, and in 64% under 40 N.s pushes. With both policies retrained from the same demonstrations, a simulated second humanoid completes box sorting in 83% of episodes without pushes.
Comments9 pages, 8 figures, 2 tables. Project page: https://hsb0508.github.io/workhorse/
