arXiv:2504.17080cs.ROcs.SY2025-04

基于SE(3)流形的统一力阻抗控制,实现安全接触与精准力跟踪。

Geometric Formulation of Unified Force-Impedance Control on SE(3) for Robotic Manipulators

  • 在SE(3)流形上构建几何统一力阻抗控制,融合能量池增强机制。
  • 仿真中成功实现位置姿态与力的联合跟踪,保持系统被动性。
  • 适用于机器人学习控制,提升样本效率,适合复杂交互任务。

本文提出一种在SE(3)流形上实现的阻抗控制框架,可同时保证力跟踪与系统被动性。基于统一力-阻抗控制(UFIC)及先前的几何阻抗控制(GIC)工作,我们发展出几何统一力阻抗控制(GUFIC),通过微分几何视角建模SE(3)流形结构。与UFIC类似,GUFIC采用能量池增广策略,确保机械臂相对于外部力的被动性,从而实现末端执行器在不确定环境中的安全接触与期望力的跟踪。此外,通过引入速度场和力场,解决了原UFIC中非因果实现的问题。由于在SE(3)上的形式化设计,所提方法继承了GIC的SE(3)不变性与等变性,有助于提升机器学习算法嵌入控制律时的采样效率。控制律在模拟环境中验证,可在需同时跟踪位置、姿态和表面作用力的场景下稳定运行。代码已公开于https://github.com/Joohwan-Seo/GUFIC_mujoco。

原文摘要 · Abstract (English)

In this paper, we present an impedance control framework on the SE(3) manifold, which enables force tracking while guaranteeing passivity. Building upon the unified force-impedance control (UFIC) and our previous work on geometric impedance control (GIC), we develop the geometric unified force impedance control (GUFIC) to account for the SE(3) manifold structure in the controller formulation using a differential geometric perspective. As in the case of the UFIC, the GUFIC utilizes energy tank augmentation for both force-tracking and impedance control to guarantee the manipulator's passivity relative to external forces. This ensures that the end effector maintains safe contact interaction with uncertain environments and tracks a desired interaction force. Moreover, we resolve a non-causal implementation problem in the UFIC formulation by introducing velocity and force fields. Due to its formulation on SE(3), the proposed GUFIC inherits the desirable SE(3) invariance and equivariance properties of the GIC, which helps increase sample efficiency in machine learning applications where a learning algorithm is incorporated into the control law. The proposed control law is validated in a simulation environment under scenarios requiring tracking an SE(3) trajectory, incorporating both position and orientation, while exerting a force on a surface. The codes are available at https://github.com/Joohwan-Seo/GUFIC_mujoco.

机器人控制力反馈几何控制强化学习

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