论文/arXiv 2609.13516·1 个月前

面向富接触机器人插入中可变阻抗控制的 Constraint-Grounded Reinforcement Learning

为免除不确定接触插入中的手动重调，CG-RL按力限和接触反馈在线调增益，并在仿真斜插入中验证。

AI 整理

• 本体 SDK 与控制
• 具身模型

原文链接：Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion（arXiv 2609.13516 · 发布日期：2026-09-11）

Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion

为免除不确定接触插入中的手动重调，CG-RL按力限和接触反馈在线调增益，并在仿真斜插入中验证。

核心要点

• 不确定接触下轴向力限和合适增益随任务变化，固定增益需手动重调。
• CG-RL根据力限和接触反馈输出残差运动、插入速率和请求增益。
• 控制器将请求增益投影到可接受范围，且不向策略暴露该范围。
• 仿真斜插入中成功率85.8±7.7%，中点固定增益基线为50.1%。

验证范围与局限：仅在仿真斜插入、五个训练种子上评估；力限满足为经验验证而非形式保证。

英文摘要 (Abstract)

In robotic insertion under uncertain contact, the axial force limit and the appropriate controller gain vary across tasks. As a result, a single fixed gain is unlikely to remain suitable across different task conditions, making conventional impedance controllers reliant on manual retuning. To eliminate manual retuning, we propose Constraint-Grounded Reinforcement Learning (CG-RL), a variable impedance framework for online gain adaptation. Conditioned on the force limit and contact feedback, the policy outputs a residual motion, an insertion rate, and a requested gain. The controller projects this gain into the admissible range without exposing the range itself to the policy. This separation allows a single policy to operate under different force limits without retraining or manual retuning. We evaluate CG-RL on simulated oblique insertion across five training seeds. CG-RL achieves an $85.8\pm7.7%$ (mean $\pm$ SD) success rate of insertions without violating the force limit, while keeping the applied gain within the admissible range. As a comparison, a fixed-gain baseline using the midpoint gain achieves a success rate of $50.1%$. The policy adapts its insertion rate continuously to the specified force limit and further generalizes to more permissive force limits above the training range. In contrast, the same actor without force-limit input does not exhibit this adaptation. The applied gain is guaranteed to remain within the admissible range, while force-limit satisfaction is validated empirically rather than guaranteed formally.

作者：Lin He, Min Deng

来源

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1. arXiv 2609.13516：Constraint-Grounded Reinforcement Learning for Variable Impedance Control in Contact-Rich Robotic Insertion访问 2026-10-21