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.

翻译：铰接工具的操作因需要协调内部自由度与密集接触交互，仍是灵巧机器人领域的一项重大挑战。尽管先前的研究主要聚焦于刚体物体，但由于铰接工具的物理复杂性以及学习功能性抓取与操作策略的难度，该类工具的使用仍未得到充分探索。我们提出Mana（操作动画师），一个通用的模拟到现实框架，将灵巧操作重新诠释为动画问题。受计算机动画启发，Mana采用由粗到精的流程，通过运动规划与强化学习将程序化生成的抓取关键帧转化为操作轨迹。该数据生成过程基本实现自动化，仅需少量鼠标点击即可指定功能可供性（每件工具用时少于1分钟）。在涵盖不同尺度与关节类型的四种铰接工具上，Mana实现了抓取与手中操作的零样本模拟到现实迁移，展示了灵巧铰接工具操作的可扩展方法。