让机器人双手学会模仿人类操作复杂物体，提升灵巧操控能力。
DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
- 用渐弱虚拟控制器引导机器人逐步接管动作，实现平滑学习。
- 在多种复杂任务中表现优于基线方法，成功率显著提升。
- 提供仿真基准与硬件对比平台，适合研究灵巧手设计者。
我们研究功能重定向问题：从人类手-物体示范中学习灵巧操控策略以跟踪目标状态。聚焦长时程、双臂操作带关节物体的任务，该问题因动作空间大、时空不连续以及人机手形态差异而极具挑战。本文提出DexMachina，一种基于课程学习的算法：核心思想是使用衰减强度的虚拟物体控制器——初始阶段物体由控制器自动驱动至目标状态，使策略在运动与接触引导下逐步接管。我们发布了一个包含多样任务与灵巧手的仿真基准，并验证DexMachina显著优于基线方法。该算法与基准支持硬件设计的功能性对比，我们基于定量与定性结果得出关键发现。随着灵巧手技术快速发展，本工作有望成为识别理想硬件特性、降低未来研究门槛的重要平台。视频与更多内容见 https://project-dexmachina.github.io/
原文摘要 · Abstract (English)
We study the problem of functional retargeting: learning dexterous manipulation policies to track object states from human hand-object demonstrations. We focus on long-horizon, bimanual tasks with articulated objects, which is challenging due to large action space, spatiotemporal discontinuities, and embodiment gap between human and robot hands. We propose DexMachina, a novel curriculum-based algorithm: the key idea is to use virtual object controllers with decaying strength: an object is first driven automatically towards its target states, such that the policy can gradually learn to take over under motion and contact guidance. We release a simulation benchmark with a diverse set of tasks and dexterous hands, and show that DexMachina significantly outperforms baseline methods. Our algorithm and benchmark enable a functional comparison for hardware designs, and we present key findings informed by quantitative and qualitative results. With the recent surge in dexterous hand development, we hope this work will provide a useful platform for identifying desirable hardware capabilities and lower the barrier for contributing to future research. Videos and more at https://project-dexmachina.github.io/
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