FoLD：面向灵巧关节物体操作的力量知情学习
FoLD: Force-Informed Learning for Dexterous Articulated Object Manipulation
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- University of Science and Technology of China（中国科学技术大学）
- Institute of AI for Industries, Chinese Academy of Sciences（中国科学院人工智能产业研究院）
- Shenzhen University（深圳大学）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
FoLD通过从人类演示中计算补偿力场生成力量先验，指导残差策略适应接触要求，在关节物体操作基准和真实机器人上优于基线，实现灵巧操作技能迁移。
中文摘要 AI 辅助
将人类演示迁移到灵巧机器人仍然具有挑战性，因为手部形态和接触动力学的差异常常导致重定向动作无法产生预期的物体行为。我们提出了FoLD，一个通过显式力量引导学习灵巧操作关节物体的框架。FoLD从人类演示中结合机器人当前的交互状态计算补偿力场，生成一个促进演示物体运动的力量先验。该力量先验指导一个残差策略，使重定向的手部动作适应任务的接触要求。我们在公开的关节物体操作基准上评估了FoLD，它在不同任务和具身上持续优于最先进的基线方法。我们进一步在真实灵巧机器人平台上验证了FoLD，展示了人类操作技能成功迁移到机器人执行。这是我们项目页面的链接：此https URL。
英文摘要
Transferring human demonstrations to dexterous robots remains challenging because differences in hand morphology and contact dynamics often cause retargeted motions to fail at producing the intended object behavior. We present \textbf{FoLD}, a framework for learning dexterous manipulation of articulated objects through explicit force guidance. FoLD compute compensatory force fields from human demonstrations together with the robot's current interaction state, yielding a force prior that promotes the demonstrated object motion. This force prior informs a residual policy that adapts retargeted hand motions to the contact requirements of the task. We evaluate FoLD on a public benchmark for articulated object manipulation, where it consistently outperforms state-of-the-art baselines across tasks and embodiments. We further validate FoLD on real dexterous robot platforms, demonstrating successful transfer of human manipulation skills to robot execution. Here is the link of our project page: https://gghgghgghgg.github.io/FoLD-project-page/.
