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：人类能够稳健地执行操作任务的卓越能力依赖于柔顺交互，这种交互通过手部结构及材料在空间上的分布式分布实现。我们提出通过在人形机器人手中模拟这种分布式柔顺性，能够提升开环操作的鲁棒性，并观察到类人行为的涌现。为实现这一目标，我们引入了配备可调柔顺性的ADAPT手，其柔顺性分布于皮肤、手指及腕部。通过大量自动化拾取-放置测试，我们证明了抓取鲁棒性接近于几何理论极限的估计值，同时对机器人手进行了800余次"应力测试"以验证其性能。最终，在受限环境中，我们对24个几何形态差异显著的物品实现了93%的成功抓取率。研究表明，手-物自组织行为是这种极端鲁棒性的核心机制——手部会根据物体几何特征自动切换抓取模式。此外，机器人抓取模式与人类自然抓取模式在直接相似度上达到68%。