论文/arXiv 2609.11775·1 个月前

通过实时雅可比估计快速学习灵巧手内握笔书写

该研究针对仿人手手内操作建模与数据需求高的问题，提出物理机器人实时任务雅可比估计控制，并在一台真机和两套仿真手上验证握笔书写。

AI 整理

• 本体 SDK 与控制
• 仿真与训练
• 数据与采集

原文链接：Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation（arXiv 2609.11775 · 发布日期：2026-09-10）

Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation

该研究针对仿人手手内操作建模与数据需求高的问题，提出物理机器人实时任务雅可比估计控制，并在一台真机和两套仿真手上验证握笔书写。

核心要点

• 动机是仿人手握持物体的灵巧手内操作仍未解决，接触复杂且动态性强。
• 方法是在物理机器人上实时估计手与物体组合系统的任务雅可比，并在线自适应控制。
• 控制器仅用笔记本CPU，约18秒初始化后开始书写，无需解析模型、仿真训练或预采集示范。
• 实验在三套仿人机械手系统上展示握笔手内关节运动，真机在空中和纸上写字母与形状，平均精度0.6毫米。

验证范围与局限：验证范围为三套仿人机械手系统，其中一台物理机器人和两套仿真系统，任务为握笔书写单笔画轨迹。

英文摘要 (Abstract)

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.

作者：Kai Stewart, Yasunori Toshimitsu, Robert Kevin Katzschmann

来源

本页事实对应的公开页面，以原文为准。

1. arXiv 2609.11775：Rapid Learning of Dexterous In-Hand Pen Writing through Real-Time Jacobian Estimation访问 2026-10-21