<ul data-eligibleForWebStory="false"><li>Soft robots, designed for surgical applications, pose challenges in modeling and control due to their nonlinear and hysteretic behavior.</li><li>Researchers have developed a hysteresis-aware neural network model for accurate prediction of the soft robot's whole-body motion, including its hysteretic behavior.</li><li>An on-policy reinforcement learning algorithm is used to train whole-body motion control strategies for the soft robotic system.</li><li>The study demonstrates significant MSE reduction and high precision in trajectory tracking for the soft robot, showing promising results for real-world clinical applications.</li></ul>

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

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