通过实时雅可比估计快速学习灵巧手内钢笔书写
Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation
- ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
提出基于实时任务雅可比估计的具身控制方法，无需模型或示范，仅用CPU在18秒内实现拟人手内钢笔书写，达到亚毫米精度，并跨三个系统验证。
AI中文摘要：
使用拟人手对抓取物体进行灵巧的手内操作是机器人灵巧性领域一个尚未解决的前沿问题。物体与手之间接触的丰富性和高度动态性，往往需要基于学习的方法进行大量的建模或数据收集工作。用于强化学习（RL）的现代模拟器无法完全复现所需的接触复杂性，而为模仿学习（IL）收集灵巧示范仍然是一个开放性问题。在本研究中，我们提出了一种基于物理机器人上手与物体组合系统的实时任务雅可比估计的具身控制方法。仅使用笔记本电脑上的CPU，所提出的控制器在约18秒的初始化后即可开始手内钢笔书写，并持续在线适应，无需解析的手-物体运动学/接触模型、模拟训练或预先收集的任务示范。我们证明了相同的估计器/控制器公式适用于三个拟人机械手系统（一个物理系统，两个模拟系统），通过一种与具体实体无关的公式，展示了抓取钢笔的人类似手内运动。在物理机器人上，无论是空中还是纸上书写的字母和形状，均实现了亚毫米级的平面内精度（各次运行平均0.6毫米）。据我们所知，这是首次展示拟人手仅通过手内运动，用抓取的钢笔书写任意单笔轨迹，并且它展示了通过计算简单和数据高效的算法实现灵巧操作，替代了如RL和IL等计算和数据密集型方法。
英文摘要：
Dexterous in-hand manipulation of a grasped object with an anthropomorphic hand is an unsolved frontier for robot dexterity. The contact-richness and highly dynamic nature of object-hand interactions tend to require extensive modeling or data-collection efforts for learning-based approaches. Modern simulators used for reinforcement learning (RL) cannot fully replicate the required contact complexity, while collecting dexterous demonstrations for imitation learning (IL) remains an open problem. In this research, we present an embodied control approach based on real-time task Jacobian estimation of the combined hand and object system on the physical robot. Using only the CPU on a laptop, the proposed controller begins in-hand pen writing after approximately 18 s of initialization and continues to adapt online, without an analytic hand--object kinematic/contact model, simulation training, or precollected task demonstrations. We demonstrate that the same estimator/controller formulation works on three anthropomorphic robotic hand systems (one physical, two simulated) to show human-like, in-hand articulation of a grasped pen by an embodiment-independent formulation. Sub-millimeter in-plane precision (mean 0.6 mm across runs) is achieved across letters and shapes written in the air and on paper on a physical robot. To our knowledge, this is the first demonstration of an anthropomorphic hand writing arbitrary single-stroke trajectories with a grasped pen through purely in-hand motion, and it showcases an alternative to compute- and data-heavy approaches such as RL and IL for achieving dexterous manipulation through computationally simple and data-efficient algorithms.
