To address traditional impedance control methods' difficulty with obtaining stable forces during robot-skin contact, a force control based on the Gaussian mixture model/Gaussian mixture regression (GMM/GMR) algorithm fusing different compensation strategies is proposed. The contact relationship between a robot end effector and human skin is established through an impedance control model. To allow the robot to adapt to flexible skin environments, reinforcement learning algorithms and a strategy based on the skin mechanics model compensate for the impedance control strategy. Two different environment dynamics models for reinforcement learning that can be trained offline are proposed to quickly obtain reinforcement learning strategies. Three different compensation strategies are fused based on the GMM/GMR algorithm, exploiting the online calculation of physical models and offline strategies of reinforcement learning, which can improve the robustness and versatility of the algorithm when adapting to different skin environments. The experimental results show that the contact force obtained by the robot force control based on the GMM/GMR algorithm fusing different compensation strategies is relatively stable. It has better versatility than impedance control, and the force error is within ~±0.2 N.

中文翻译：

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基于融合不同补偿策略的GMM/GMR算法的机器人力控制研究

针对传统阻抗控制方法在机器人与皮肤接触过程中难以获得稳定力的问题，提出一种融合不同补偿策略的基于高斯混合模型/高斯混合回归（GMM/GMR）算法的力控制。通过阻抗控制模型建立机器人末端执行器与人体皮肤之间的接触关系。为了让机器人适应灵活的皮肤环境，强化学习算法和基于皮肤力学模型的策略补偿了阻抗控制策略。提出了两种可以离线训练的不同的强化学习环境动力学模型，以快速获得强化学习策略。基于GMM/GMR算法融合三种不同的补偿策略，利用物理模型的在线计算和强化学习的离线策略，可以提高算法适应不同皮肤环境时的鲁棒性和通用性。实验结果表明，基于GMM/GMR算法融合不同补偿策略的机器人力控制得到的接触力较为稳定。比阻抗控制具有更好的通用性，力误差在~±0.2 N以内。