手指作为腿：用拟人手学习自支撑的运动与操作
Fingers as Legs: Learning Self-Supported Locomotion and Manipulation with an Anthropomorphic Hand
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中文总结 AI 辅助
本研究利用强化学习使拟人手以同一组手指实现自支撑运动与操作，在模拟和硬件上验证了爬行、转向、恢复及键盘与视觉任务，展示了无需额外机构的紧凑移动操作器。
中文摘要 AI 辅助
一个会行走的机器人手必须使用同一组手指来移动身体、支撑体重并与环境互动。我们展示了拟人手如何在保留其手指设计和位置控制器的同时学习这些技能。机载电源和计算使平台自包含。我们的强化学习方法考虑了手的不等长手指，在根据硬件测量校准的模拟器中进行训练。在模拟中，我们的奖励公式使手移动得比最初为四足动物设计的调优奖励更快。在硬件上，任务特定的策略实现了无束缚的爬行、转向和跌倒恢复。在支撑自身重量的同时，手还能在没有视觉的情况下执行连续的键盘命令，并使用头顶视觉反馈将物体推到目标位置。这些结果展示了一个紧凑的移动操作器，它重新利用手指进行运动和互动，而无需单独的运动机构。
英文摘要
A walking robotic hand must use the same fingers to move its body, support its weight, and interact with the environment. We show how an anthropomorphic hand can learn these skills while retaining its finger design and position controller. Onboard power and computation make the platform self-contained. Our reinforcement learning approach accounts for the hand's unequal fingers, with training in a simulator calibrated from hardware measurements. In simulation, the hand moves faster with our reward formulation than with tuned rewards originally designed for quadrupeds. On hardware, task-specific policies enable untethered crawling, steering, and fall recovery. While supporting its own weight, the hand also executes successive keyboard commands without vision and pushes an object to targets using overhead visual feedback. These results demonstrate a compact mobile manipulator that reuses its fingers for locomotion and interaction, without a separate locomotion mechanism.
发表机构
- ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
