让机器人操作更柔顺，减少接触时的冲击力和波动。
MDIR: A Task-Manifold Impedance Retargeting Method for Contact-Rich Teleoperation
- 通过分解任务通道响应，动态调整阻抗参数以适应不同接触场景。
- 在15次闭环实验中全部通过任务验证，且力峰值、冲量等指标全面下降。
- 适合需要高精度接触控制的机器人操作，如装配、擦拭等任务。
固定笛卡尔阻抗使富含接触的操作演示变得可行，但其带来的进展与接触支撑也决定了作用力和力变异性。本文研究单次演示下的控制器到控制器阻抗重定向问题。给定一个固定的笛卡尔阻抗指令序列 {K0, D0, xcmd}，基于流形分解的阻抗重定向（MDIR）方法可确定性地将记录的控制器重参数化为可执行的任务通道可变阻抗指令。MDIR通过保持沿演示轨迹附近的投影任务通道响应来解决这一局部重定向问题。它在操作功、用力和支撑通道中，以控制链度量下的被动残差补全形式表示源响应，计算出可执行的笛卡尔到流形重定向（C2M）基准，并应用流形约束参数优化（MPO）选择一个可行的代表解，使其腕部力峰值、冲量、力变异性及标称控制器功率均更低。在Franka Panda机械臂上进行的平面擦拭、抓取放置和推移任务测试中，完整MDIR控制器在全部15次闭环执行中均通过任务检查，且所有四项激进性指标均低于固定阻抗演示结果。
原文摘要 · Abstract (English)
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.
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