Hysteresis-Aware Neural Network Modeling and Whole-Body Reinforcement Learning Control of Soft Robots

2025-04-18

AI Summary

Soft robotic surgical systems offer superior compliance and safety, yet their inherent material hysteresis introduces significant nonlinearities, leading to inaccurate dynamics modeling and poor control precision. To address this, we propose a hysteresis-aware whole-body neural network dynamics model—the first to embed hysteresis compensation directly into an end-to-end learning framework. We further develop a highly parallel soft-body simulation environment, integrated with Proximal Policy Optimization (PPO) for closed-loop motion control, enabling seamless sim-to-real transfer. Experimental results demonstrate an 84.95% reduction in mean squared error (MSE) for hysteresis modeling; real-world trajectory tracking achieves sub-millimeter accuracy (0.126–0.250 mm); and sub-millimeter precision is validated in phantom-based laser ablation surgery, confirming clinical feasibility. This work establishes a novel, generalizable, high-accuracy, and deployable paradigm for dynamics modeling and control of soft surgical robots.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

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.

Problem

• Modeling nonlinear hysteretic behavior in soft robots
• Developing precise control for soft robotic surgical systems
• Reducing tracking errors in real-world surgical applications

Innovation

• Hysteresis-aware neural network for soft robots
• Whole-body reinforcement learning control strategy
• High-precision parallel simulation environment