Variable impedance control (VIC) endows robots with the ability to adjust their compliance, enhancing safety and adaptability in contact-rich tasks. However, determining suitable variable impedance parameters for specific tasks remains challenging. To address this challenge, this paper proposes an imitation learning-based VIC policy that employs observations integrated with RGBD and force/torque (F/T) data enabling a collaborative mobile manipulator to execute contact-rich tasks by learning from human demonstrations. The VIC policy is learned through training the robot in a customized simulation environment, utilizing an inverse reinforcement learning (IRL) algorithm. High-dimensional demonstration data is encoded by integrating a 16-layer convolutional neural network (CNN) into the IRL environment. To minimize the sim-to-real gap, contact dynamic parameters in the training environment are calibrated. Then, the learning-based VIC policy is comprehensively trained in the customized environment and its transferability is validated through an industrial production case involving a high precision peg-in-hole task using a collaborative mobile manipulator. The training and testing results indicate that the proposed imitation learning-based VIC policy ensures robust performance for contact-rich tasks.

中文翻译：

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协作式工业移动机械手的接触式操作的可变阻抗控制：一种仿制学习方法


可变阻抗控制 （VIC） 使机器人能够调整其柔度，从而提高接触密集型任务的安全性和适应性。然而，为特定任务确定合适的可变阻抗参数仍然具有挑战性。为了应对这一挑战，本文提出了一种基于模仿学习的 VIC 策略，该策略采用与 RGBD 和力/扭矩 （F/T） 数据集成的观察，使协作式移动机械手能够通过从人类演示中学习来执行接触丰富的任务。VIC 策略是通过在自定义仿真环境中训练机器人，利用逆向强化学习 （IRL） 算法来学习的。通过将 16 层卷积神经网络 （CNN） 集成到 IRL 环境中，对高维演示数据进行编码。为了最小化 sim-to-real 的差距，校准了训练环境中的接触动态参数。然后，在定制环境中对基于学习的 VIC 策略进行全面训练，并通过涉及使用协作移动机械手的高精度 Peg-in-hole 任务的工业生产案例来验证其可转移性。训练和测试结果表明，拟议的基于模仿学习的 VIC 策略确保了接触丰富的任务的稳健性能。