MDIR：面向接触丰富型遥操作的任务流形阻抗重定向方法
MDIR: A Task-Manifold Impedance Retargeting Method for Contact-Rich Teleoperation
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中文总结 AI 辅助
本研究提出MDIR方法，实现单演示下控制器间阻抗重定向，在Franka Panda机器人的三类任务中，该方法可降低腕力峰值等四项激进性指标，且15次闭环执行均通过任务检查。
中文摘要 AI 辅助
固定笛卡尔阻抗使接触丰富型遥操作演示具备实用性，但保障进展与接触支持的增益也决定了冲击力和力的变异性。本研究针对单演示的控制器间阻抗重定向问题，给定一组固定笛卡尔阻抗命令序列{K0, D0, xcmd}，流形分解阻抗重定向（MDIR）确定性地将记录的控制器重新参数化为可执行的任务通道变阻抗命令。MDIR通过保留演示轨迹附近的投影任务通道响应该局部重定向问题，在控制链度量下用被动残差补集表示操作工作、用力和支持通道中的源响应，计算可执行的笛卡尔至流形重定向（C2M）基线，并应用流形约束参数优化（MPO）选择可行代表，以降低腕力峰值、冲量、力变异性和标称控制器功率。在Franka Panda机器人上的平面擦拭、抓取放置和推压任务中，完整的MDIR控制器在15次闭环执行中均通过任务检查，且相对于固定阻抗演示降低了全部四项激进性指标。
英文摘要
Fixed Cartesian impedance makes contact-rich teleoperation demonstrations practical, but gains that secure progress and contact support also determine impact and force variability. We study single-demonstration controller-to-controller impedance retargeting. Given one fixed Cartesian impedance command sequence {K0, D0, xcmd}, Manifold-Decomposed Impedance Retargeting (MDIR) deterministically reparameterizes the recorded controller into an executable task-channel variable-impedance command. MDIR targets this local retargeting problem by preserving projected task-channel responses near the demonstrated trajectory. It represents the source response in operational work, exertion, and support channels with a passive residual complement under a control-chain metric, computes an executable Cartesian-to-Manifold Retargeting (C2M) baseline, and applies Manifold-Constrained Parameter Optimization (MPO) to select a feasible representative with lower wrist-force peaks, impulse, force variability, and nominal controller power. Across planar wiping, pick-and-place, and pushing on a Franka Panda, the full MDIR controller passes Task Check in all 15 closed-loop executions and reduces all four aggressiveness metrics relative to the fixed-impedance demonstrations.
