A KMP-based interactive learning approach for robot trajectory adaptation with obstacle avoidance
ISSN: 0143-991X
Article publication date: 18 January 2024
Issue publication date: 23 February 2024
Abstract
Purpose
Imitation learning is a powerful tool for planning the trajectory of robotic end-effectors in Cartesian space. Present methods can adapt the trajectory to the obstacle; however, the solutions may not always satisfy users, whereas it is hard for a nonexpert user to teach the robot to avoid obstacles in time as he/she wishes through demonstrations. This paper aims to address the above problem by proposing an approach that combines human supervision with the kernelized movement primitives (KMP) model.
Design/methodology/approach
This approach first extracts the reference database used to train KMP from demonstrations by using Gaussian mixture model and Gaussian mixture regression. Subsequently, KMP is used to modulate the trajectory of robotic end-effectors in real time based on feedback from its interaction with humans to avoid obstacles, which benefits from a novel reference database update strategy. The user can test different obstacle avoidance trajectories in the current task until a satisfactory solution is found.
Findings
Experiments performed with the KUKA cobot for obstacle avoidance show that this approach can adapt the trajectories of the robotic end-effector to the user’s wishes in real time, including trajectories that the robot has already passed and has not yet passed. Simulation comparisons also show that it exhibits better performance than KMP with the original reference database update strategy.
Originality/value
An interactive learning approach based on KMP is proposed and verified, which not only enables users to plan the trajectory of robotic end-effectors for obstacle avoidance more conveniently and efficiently but also provides an effective idea for accomplishing interactive learning tasks under constraints.
Keywords
Acknowledgements
This work was supported by the National Key Research and Development Project under Grant 2018YFB1308603 and by Fujian Province Industry Academia Cooperation Project under Grant 2022H6016.
Citation
Xiao, S., Chen, X., Lu, Y., Ye, J. and Wu, H. (2024), "A KMP-based interactive learning approach for robot trajectory adaptation with obstacle avoidance", Industrial Robot, Vol. 51 No. 2, pp. 326-339. https://doi.org/10.1108/IR-11-2023-0284
Publisher
:Emerald Publishing Limited
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