A Novel Actor—Critic Motor Reinforcement Learning for Continuum Soft Robots

Author:

Pantoja-Garcia Luis1ORCID,Parra-Vega Vicente1ORCID,Garcia-Rodriguez Rodolfo2ORCID,Vázquez-García Carlos Ernesto1ORCID

Affiliation:

1. Robotics and Advanced Manufacturing Department, Research Center for Advanced Studies (Cinvestav-Ipn), Ramos Arizpe 25903, Mexico

2. Facultad de Ciencias de la Administración, Universidad Autónoma de Coahuila, Saltillo 25280, Mexico

Abstract

Reinforcement learning (RL) is explored for motor control of a novel pneumatic-driven soft robot modeled after continuum media with a varying density. This model complies with closed-form Lagrangian dynamics, which fulfills the fundamental structural property of passivity, among others. Then, the question arises of how to synthesize a passivity-based RL model to control the unknown continuum soft robot dynamics to exploit its input–output energy properties advantageously throughout a reward-based neural network controller. Thus, we propose a continuous-time Actor–Critic scheme for tracking tasks of the continuum 3D soft robot subject to Lipschitz disturbances. A reward-based temporal difference leads to learning with a novel discontinuous adaptive mechanism of Critic neural weights. Finally, the reward and integral of the Bellman error approximation reinforce the adaptive mechanism of Actor neural weights. Closed-loop stability is guaranteed in the sense of Lyapunov, which leads to local exponential convergence of tracking errors based on integral sliding modes. Notably, it is assumed that dynamics are unknown, yet the control is continuous and robust. A representative simulation study shows the effectiveness of our proposal for tracking tasks.

Publisher

MDPI AG

Subject

Artificial Intelligence,Control and Optimization,Mechanical Engineering

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Enhancing Robotic Control: A TD3-Based Approach for Planar Continuum Robots;2024 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST);2024-04-11

2. DDPG-Based Adaptive Sliding Mode Control with Extended State Observer for Multibody Robot Systems;Robotics;2023-11-26

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