将复杂工具操作转化为动画生成问题，实现零样本跨域迁移。
Mana: Dexterous Manipulation of Articulated Tools
- 用粗到细的动画式流程，将抓握关键帧转为操作轨迹。
- 四类不同工具均实现零样本从仿真到现实的稳定操作。
- 仅需数秒点击定义功能属性，适合快速部署新工具任务。
关节式工具的操作仍是灵巧机器人中的重大挑战，因需协调内部自由度并处理丰富的接触交互。以往研究多集中于刚性物体，而关节工具因物理复杂性和难以学习功能性抓取与操作策略，仍研究不足。本文提出Mana（Manipulation Animator），一种通用的仿真到现实框架，将灵巧操作重新定义为动画生成问题。受计算机动画启发，Mana采用自粗至精的流程，通过运动规划与强化学习将程序生成的抓握关键帧转化为操作轨迹。数据生成过程基本自动化，仅需鼠标点击指定功能属性（每工具<1分钟）。在四种涵盖不同尺度和关节类型的关节工具上，Mana实现了抓取与手内操作的零样本仿真到现实迁移，验证了其在灵巧关节工具使用上的可扩展性。
原文摘要 · Abstract (English)
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.
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