TY - JOUR
T1 - Variable impedance control on contact-rich manipulation of a collaborative industrial mobile manipulator
T2 - An imitation learning approach
AU - Zhou, Zhengxue
AU - Yang, Xingyu
AU - Zhang, Xuping
PY - 2025/4
Y1 - 2025/4
N2 - 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.
AB - 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.
KW - Collaborative Mobile Manipulator
KW - Contact-rich Manipulation
KW - Imitation Learning
KW - Variable Impedance Control
UR - http://www.scopus.com/inward/record.url?scp=85208062749&partnerID=8YFLogxK
U2 - 10.1016/j.rcim.2024.102896
DO - 10.1016/j.rcim.2024.102896
M3 - Journal article
AN - SCOPUS:85208062749
SN - 0736-5845
VL - 92
JO - Robotics and Computer-Integrated Manufacturing
JF - Robotics and Computer-Integrated Manufacturing
M1 - 102896
ER -