发表机构
National University of Defense Technology; National Key Laboratory of Equipment State Sensing and Smart Support（国防科技大学; 装备状态感知与智能保障全国重点实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对仿生刚柔耦合手指的强非线性和分辨率敏感问题，提出基于条件流匹配的雅可比流匹配框架，学习分辨率一致的雅可比场，通过ODE积分实现跨时间分辨率一致推理，显著降低预测误差并提升长时域轨迹保真度。
AI 中文摘要
受生物启发的腱驱动刚柔耦合灵巧手指表现出强非线性和对构型的敏感性，这使得精确建模具有挑战性。在离散时间控制中，基于雅可比矩阵的运动学算法通常依赖于逐点局部线性近似，这使其性能对传感器采样频率和控制器更新频率敏感。为解决此问题，我们提出了雅可比流匹配（JFM），一种基于条件流匹配（CFM）的结构化学习框架，用于学习分辨率一致的雅可比场，将驱动到运动的转换建模为动态流。所提出的框架支持通过ODE积分进行单步预测和连续展开，从而在不同时间分辨率下实现一致的推理。在腱驱动刚柔耦合手指上的实验表明，所提出的方法抑制了异常值误差并提高了单步预测精度，与基线离散雅可比学习方法相比，全局平均RMSE降低了超过53%。对于长时域预测，通过ODE积分恢复的轨迹在稀疏采样（步长=8）下实现了更高的保真度，RMSE中位数降低了14.43%，误差方差降低了24.87%。这些结果表明，所学习的基于流的雅可比场为刚柔耦合非线性系统中的离线多步轨迹优化提供了有效的局部模型。
英文摘要
Bio-inspired tendon-driven rigid-soft coupled dexterous fingers exhibit strong nonlinearity and configuration-dependent sensitivity, making accurate modeling challenging. In discrete-time control, Jacobian-based kinematic algorithms typically rely on point-wise local linear approximations, which makes their performance sensitive to sensor sampling frequency and controller update frequency. To address this issue, we propose Jacobian Flow Matching (JFM), a structured learning framework based on Conditional Flow Matching (CFM), to learn a resolution-consistent Jacobian field that models actuation-to-motion transitions as a dynamical flow. The proposed framework supports both single-step prediction and continuous rollout via ODE integration, enabling consistent inference across temporal resolutions. Experiments on a tendon-driven rigid-soft finger show that the proposed method suppresses outlier errors and improves single-step prediction accuracy, reducing the global average RMSE by over 53% compared with a baseline discrete Jacobian learning approach. For long-horizon prediction, trajectories recovered via ODE integration achieve higher fidelity under sparse sampling (Stride = 8), reducing the RMSE median by 14.43% and the error variance by 24.87%. These results demonstrate that the learned flow-based Jacobian field provides an effective local model for offline multi-step trajectory optimization in rigid-soft coupled nonlinear systems.
Comments27 pages, 8 figures, including supplementary material. Under review at Robotics and Autonomous Systems
