Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots
Journal:
arXiv
Published Date:
Apr 18, 2025
Abstract
Soft robots exhibit inherent compliance and safety, which makes them
particularly suitable for applications requiring direct physical interaction
with humans, such as surgical procedures. However, their nonlinear and
hysteretic behavior, resulting from the properties of soft materials, presents
substantial challenges for accurate modeling and control. In this study, we
present a soft robotic system designed for surgical applications and propose a
hysteresis-aware whole-body neural network model that accurately captures and
predicts the soft robot's whole-body motion, including its hysteretic behavior.
Building upon the high-precision dynamic model, we construct a highly parallel
simulation environment for soft robot control and apply an on-policy
reinforcement learning algorithm to efficiently train whole-body motion control
strategies. Based on the trained control policy, we developed a soft robotic
system for surgical applications and validated it through phantom-based laser
ablation experiments in a physical environment. The results demonstrate that
the hysteresis-aware modeling reduces the Mean Squared Error (MSE) by 84.95
percent compared to traditional modeling methods. The deployed control
algorithm achieved a trajectory tracking error ranging from 0.126 to 0.250 mm
on the real soft robot, highlighting its precision in real-world conditions.
The proposed method showed strong performance in phantom-based surgical
experiments and demonstrates its potential for complex scenarios, including
future real-world clinical applications.
