Learning Compliant Assembly Strategy From Demonstration

Sheng Liu1; Juyi Sheng2; Yongsheng Ou2

10.1109/RCAR58764.2023.10249450

2023

979-8-3503-2719-9

2023 IEEE International Conference on Real-time Computing and Robotics (RCAR)

929-934

17-20 July 2023

Datong, China

Compared with robots, humans can complete the different assembly tasks of parts flexibly and quickly. By teaching robots with human experiences, not only the industrial assembling tasks can be resolved, but also many other robot applications can be realized. The pose adjustment stage is the most critical part of the peg-in-hole assembly process. This paper analyzes the contact force in the pose adjustment stage and calculates the control variables that affect the assembly motion. Based on the Gaussian mixture model (GMM), the nonlinear mapping relationship between the control variables and the state variables during the assembly is established using the human demonstration data, and the parameters of the model are solved by the expectation maximization (EM) algorithm, and thus the human-like compliant assembly is completed by a robot. In order to verify the effectiveness of the demonstration-learning algorithm, a peg-in-hole assembly experiment was carried out using KUKA manipulator. Finally, the experiment shows that the proposed learning-based method not only improves the efficiency of the robot peg-in-hole assembly but also makes the manipulator have a satisfied adaptive ability to the complex environments in the assembly process.

Learning systems Service robots Force Education Manipulators Real-time systems Data models

第一

EI

20234214881183

Assembly ; Demonstrations ; Gaussian distribution ; Learning algorithms ; Learning systems ; Maximum principle ; Object recognition ; Robot applications

Machine Learning:723.4.2 ; Robot Applications:731.6 ; Probability Theory:922.1 ; Mathematical Statistics:922.2

IEEE

1.Southern University of Science and Technology (SUSTech)
2.Guangdong Provincial Key Laboratory of Robotics and Intelligent System, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Sheng Liu,Juyi Sheng,Yongsheng Ou. Learning Compliant Assembly Strategy From Demonstration[C],2023:929-934.