Acquiring human skills offers an efficient approach to tackle complex task planning challenges. When performing a learned skill model for a continuous contact task, such as robot polishing in an uncertain environment, the robot needs to be able to adaptively modify the skill model to suit the environment and perform the desired task. The environmental perturbation of the polishing task is mainly reflected in the variation of contact force. Therefore, adjusting the task skill model by providing feedback on the contact force deviation is an effective way to meet the task requirements. In this study, a phase-modulated diagonal recurrent neural network (PMDRNN) is proposed for force feedback model learning in the robotic polishing task. The contact between the tool and the workpiece in the polishing task can be considered a dynamic system. In comparison to the existing feedforward neural network phase-modulated neural network (PMNN), PMDRNN combines the diagonal recurrent network structure with the phase-modulated neural network layer to improve the learning performance of the feedback model for dynamic systems. Specifically, data from real-world robot polishing experiments are used to learn the feedback model. PMDRNN demonstrates a significant reduction in the training error of the feedback model when compared to PMNN. Building upon this, the combination of PMDRNN and dynamic movement primitives (DMPs) can be used for real-time adjustment of skills for polishing tasks and effectively improve the robustness of the task skill model. Finally, real-world robotic polishing experiments are conducted to demonstrate the effectiveness of the approach.

翻译：获取人类技能为应对复杂任务规划挑战提供了一种高效途径。当在不确定环境中执行机器人抛光等连续接触任务的已习得技能模型时，机器人需要能够自适应地修改技能模型以适应环境并完成期望任务。抛光任务的环境扰动主要体现在接触力的变化上。因此，通过反馈接触力偏差来调整任务技能模型是满足任务需求的有效方法。本研究提出了一种相位调制对角递归神经网络（PMDRNN），用于机器人抛光任务中的力反馈模型学习。抛光任务中工具与工件之间的接触可视为动态系统。与现有前馈神经网络相位调制神经网络（PMNN）相比，PMDRNN将对角递归网络结构与相位调制神经网络层相结合，以提升动态系统反馈模型的学习性能。具体而言，利用真实机器人抛光实验数据学习反馈模型。相较于PMNN，PMDRNN使反馈模型的训练误差显著降低。在此基础上，将PMDRNN与动态运动基元（DMPs）相结合，可用于实时调整抛光任务技能，并有效提升任务技能模型的鲁棒性。最后，通过真实机器人抛光实验验证了本方法的有效性。