[论文] Mana: Dexterous Manipulation of Articulated Tools

论文概要

研究领域: CV 作者: Zhao-Heng Yin, Guanya Shi, Pieter Abbeel 发布时间: 2025-06-13 arXiv: 2506.10668

中文摘要

由于需要协调内部自由度和接触丰富的交互，铰接工具操作仍然是灵巧机器人领域的主要挑战。先前工作主要关注刚性物体，铰接工具使用因其物理复杂性和学习功能性抓取与操作策略的难度而未被充分探索。我们提出Mana（Manipulation Animator），一个通用的sim-to-real框架，将灵巧操作重新诠释为动画问题。受计算机动画启发，Mana采用从粗到细的管道，通过运动规划和强化学习将程序生成的抓取关键帧转换为操作轨迹。数据生成过程大部分是自动的，只需几次鼠标点击指定功能可供性（每个工具<1分钟）。在跨越不同规模和关节类型的四种铰接工具上，Mana在抓取和手中操作两方面都实现了零样本sim-to-real迁移，展示了一种可扩展的灵巧铰接工具使用方法。

原文摘要

Articulated tool manipulation remains a major challenge in dexterous robotics due to the need to coordinate internal degrees of freedom and contact-rich interactions. While prior work has largely focused on rigid objects, articulated tool use remains underexplored because of its physical complexity and the difficulty of learning functional grasping and manipulation policies. We present Mana (Manipulation Animator), a general sim-to-real framework that reinterprets dexterous manipulation as an animation problem. Inspired by computer animation, Mana employs a coarse-to-fine pipeline that transforms procedurally-generated grasp keyframes into manipulation trajectories through motion planning and reinforcement learning. The data generation process is largely automatic, requiring only a few mou...

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