Model-Based Reinforcement Learning for Trajectory Tracking of Musculoskeletal Robots

被引：2

作者：

Xu, Haoran ^{[1
]}

Fan, Jianyin ^{[1
]}

Wang, Qiang ^{[1
]}

机构：

[1] Harbin Inst Technol, Dept Control Sci & Engn, Harbin, Peoples R China

来源：

2023 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC | 2023年

基金：

中国国家自然科学基金;

关键词：

model learning; model-based reinforcement learning; pneumatic actuators; ARM DRIVEN; MUSCLE;

D O I：

10.1109/I2MTC53148.2023.10175993

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper aims to solve the trajectory tracking task of the pneumatic musculoskeletal robot within a model-based reinforcement learning framework. Considering the limited sensors and short lifespan of self-made pneumatic artificial muscles, multi-task Gaussian process regression is employed for micro-data model learning and the learned model is combined with cross entropy method (CEM)-based model predictive control to plan for the optimal action online. To further compensate for the model imperfection and improve the control performance, a proportional derivative controller-like strategy is proposed to guide the search space of the original CEM solver. The effectiveness of our approach is verified on a real musculoskeletal system with one degree of freedom and the results show that only 50 s of interacting with the environment is enough for the robot to learn trajectory tracking skills from scratch.

引用

页数：6

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