Barreiros Jose A, Önol Aykut Özgün, Zhang Mengchao, Creasey Sam, Goncalves Aimee, Beaulieu Andrew, Bhat Aditya, Tsui Kate M, Alspach Alex
Toyota Research Institute, Cambridge, MA, USA.
Sci Robot. 2025 Aug 20;10(105):eads6790. doi: 10.1126/scirobotics.ads6790.
Humans use diverse skills and strategies to effectively manipulate various objects, ranging from dexterous in-hand manipulation (fine motor skills) to complex whole-body manipulation (gross motor skills). The latter involves full-body engagement and extensive contact with various body parts beyond just the hands, where the compliance of our skin and muscles plays a crucial role in increasing contact stability and mitigating uncertainty. For robots, synthesizing these contact-rich behaviors has fundamental challenges because of the rapidly growing combinatorics inherent to this amount of contact, making explicit reasoning about all contact interactions intractable. We explore the use of example-guided reinforcement learning to generate robust whole-body skills for the manipulation of large and unwieldy objects. Our method's effectiveness is demonstrated on Toyota Research Institute's Punyo robot, a humanoid upper body with highly deformable, pressure-sensing skin. Training was conducted in simulation with only a single example motion per object manipulation task, and policies were easily transferred to hardware owing to domain randomization and the robot's compliance. The resulting agent can manipulate various everyday objects, such as a water jug and large boxes, in a similar fashion to the example motion. In addition, we show blind dexterous whole-body manipulation, relying solely on proprioceptive and tactile feedback without object pose tracking. Our analysis highlights the critical role of compliance in facilitating whole-body manipulation with humanoid robots.
人类运用多样的技能和策略来有效操控各种物体，从灵巧的手部操作（精细运动技能）到复杂的全身操作（粗大运动技能）。后者需要全身参与，并涉及除手部之外的各种身体部位的广泛接触，在这种情况下，我们皮肤和肌肉的顺应性对于提高接触稳定性和减少不确定性起着至关重要的作用。对于机器人而言，合成这些富含接触的行为面临着根本性挑战，因为这种大量接触所固有的组合复杂性迅速增加，使得对所有接触交互进行显式推理变得难以处理。我们探索使用示例引导的强化学习来生成用于操控大型且笨重物体的强大全身技能。我们的方法在丰田研究院的普尼奥机器人上得到了验证，这是一个具有高度可变形压力感应皮肤的类人上半身机器人。训练仅在模拟环境中针对每个物体操作任务使用单个示例运动进行，并且由于领域随机化和机器人的顺应性，策略能够轻松转移到硬件上。由此产生的智能体能够以与示例运动类似的方式操控各种日常物体，如水壶和大箱子。此外，我们展示了仅依靠本体感觉和触觉反馈而无需物体位姿跟踪的盲灵巧全身操作。我们的分析突出了顺应性在促进类人机器人全身操作中的关键作用。
