Junge Kai, Hughes Josie
CREATE Lab, EPFL, Lausanne, Switzerland.
Commun Eng. 2025 Apr 26;4(1):76. doi: 10.1038/s44172-025-00407-4.
The impressive capabilities of humans to robustly perform manipulation stems from compliant interactions, enabled by the structure and materials distributed in the hands. We propose that mimicking this spatially distributed compliance in an anthropomorphic robotic hand enhances open-loop manipulation robustness and leads to human-like behaviors. Here we introduce the ADAPT Hand, equipped with configurable compliant elements on the skin, fingers, and wrist. After quantifying the effect of compliance on individual components against a rigid configuration, we experimentally analyze the performance of the full hand. Through automated pick-and-place tests, we show the grasping robustness mirrors the estimated geometric theoretical limit, while stress-testing the robot to perform 800+ grasps. Finally, 24 items with varying geometries are grasped in a constrained environment with a 93% success rate. We demonstrate that the hand-object self-organization behavior, driven by passive adaptation, underpins this robustness. The hand exhibits different grasp types based on object geometries, with a 68% similarity to natural human grasps.
人类能够强有力地进行操作,这一令人印象深刻的能力源于手部所分布的结构和材料所实现的柔顺交互。我们提出,在拟人化机器人手中模仿这种空间分布的柔顺性,可增强开环操作的稳健性,并产生类人行为。在此,我们介绍了ADAPT手,它在皮肤、手指和手腕上配备了可配置的柔顺元件。在针对刚性配置量化了柔顺性对各个组件的影响之后,我们对整只手的性能进行了实验分析。通过自动抓取和放置测试,我们表明抓取稳健性反映了估计的几何理论极限,同时对机器人进行压力测试以执行800多次抓取。最后,在受限环境中对24种不同几何形状的物品进行抓取,成功率达到93%。我们证明,由被动适应驱动的手-物体自组织行为是这种稳健性的基础。这只手根据物体的几何形状表现出不同的抓取类型,与人类自然抓取的相似度为68%。