DexMachina：面向双手灵巧操作的功能性重定向
DexMachina: Functional Retargeting for Bimanual Dexterous Manipulation
AI总结：
针对包含铰接物体的长时程双手灵巧操作任务，提出基于课程学习的DexMachina算法，利用强度衰减的虚拟控制器引导策略接管控制，并发布仿真基准，显著超越基线方法且支持硬件设计比较。
摘要：
我们研究功能性重定向问题：从人手与物体交互的演示中学习灵巧操作策略，以跟踪物体状态。我们重点关注包含铰接物体的长时程双手任务，由于动作空间大、时空不连续性以及人手与机器手之间的具身差异，该任务极具挑战性。我们提出了DexMachina，一种基于课程学习的新颖算法：其核心思想是使用强度逐渐衰减的虚拟物体控制器：物体首先被自动驱动至目标状态，使得策略能够在运动和接触引导下逐渐学会接管控制。我们发布了一个仿真基准，包含多种任务和灵巧手，并表明DexMachina显著优于基线方法。我们的算法和基准支持对硬件设计进行功能性比较，并基于定量和定性结果提出了关键发现。随着近期灵巧手开发的激增，我们希望这项工作能为识别理想的硬件能力提供有用平台，并降低参与未来研究的门槛。
英文摘要：
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/
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
