Variable Impedance Control on Contact-Rich Manipulation of a Collaborative Industrial Mobile Manipulator: An Imitation Learning Approach

Abstract

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 represented 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