The impressive capabilities of humans to robustly perform manipulation relies on compliant interactions, enabled through the structure and materials spatially distributed in our hands. We propose by mimicking this distributed compliance in an anthropomorphic robotic hand, the open-loop manipulation robustness increases and observe the emergence of human-like behaviours. To achieve this, we introduce the ADAPT Hand equipped with tunable compliance throughout the skin, fingers, and the wrist. Through extensive automated pick-and-place tests, we show the grasping robustness closely mirrors an estimated geometric theoretical limit, while `stress-testing' the robot hand to perform 800+ grasps. Finally, 24 items with largely varying geometries are grasped in a constrained environment with a success rate of 93\%. We demonstrate the hand-object self-organization behavior underlines this extreme robustness, where the hand automatically exhibits different grasp types depending on object geometries. Furthermore, the robot grasp type mimics a natural human grasp with a direct similarity of 68\%.

翻译：人类之所以能实现鲁棒的操控行为，得益于手部结构及材料在空间上的分布所造就的柔顺交互能力。本文提出通过在人形机器人手部模拟这种分布式柔顺性，可提升开环操控鲁棒性并观察到类人行为的涌现。为此，我们设计了配备可调柔顺性的ADAPT手爪，其柔顺性分布于皮肤、手指及腕部。通过大量自动化抓取-放置测试，我们证明抓取鲁棒性逼近几何理论极限的估计值，同时对手爪进行800余次抓取的"压力测试"。最后，在受约束环境中对24种几何形态差异显著的物体进行抓取，成功率达93%。研究表明，手-物自组织行为是这种极端鲁棒性的基础——手爪会根据物体几何形状自动呈现不同的抓取模式。此外，机器人抓取模式与人类自然抓取的直接相似度达68%。