Mana: 铰接工具的灵巧操作
Mana: Dexterous Manipulation of Articulated Tools
AI总结：
提出Mana框架，将灵巧操作重解释为动画问题，通过粗到细的流水线自动生成操作轨迹，实现铰接工具的零样本仿真到现实迁移。
摘要：
铰接工具的操作由于需要协调内部自由度与接触丰富的交互，仍然是灵巧机器人学中的一个主要挑战。虽然先前的工作主要集中在刚性物体上，但铰接工具的使用由于其物理复杂性以及学习功能性抓取和操作策略的困难，仍未得到充分探索。我们提出了Mana（操作动画器），一个通用的仿真到现实框架，将灵巧操作重新解释为动画问题。受计算机动画启发，Mana采用粗到细的流水线，通过运动规划和强化学习将程序生成的抓取关键帧转化为操作轨迹。数据生成过程基本自动化，仅需几次鼠标点击即可指定功能可供性（每个工具不到1分钟）。在跨越不同尺度和关节类型的四个铰接工具上，Mana实现了抓取和手内操作的零样本仿真到现实迁移，展示了灵巧铰接工具操作的可扩展方法。
英文摘要：
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 mouse clicks to specify functional affordances (<1 minute per tool). Across four articulated tools spanning different scales and joint types, Mana achieves zero-shot sim-to-real transfer for both grasping and in-hand manipulation, demonstrating a scalable approach to dexterous articulated tool use.
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。
