A Framework for Soft Robot Control: Integrating Physics-Based Modeling with Exploration Based Learning

Abstract

Soft robots present unique challenges for accurate modeling and control due to their virtually infinite degrees of freedom and highly nonlinear deformations. High-fidelity continuum models offer accuracy but are often computationally prohibitive for real-time control, while purely learning-based policies are efficient yet frequently lack robustness and require extensive data collection. In this paper, we propose a hybrid control framework that trains Reinforcement Learning (RL) policies using the physics-based Geometric Variable-Strain (GVS) formulation. This integration enables a Proximal Policy Optimization (PPO) agent to learn on a compact, physically exact state parameterization within the SoRoSim environment, leveraging continuum mechanics for accuracy without the need for real-world data collection. We validate our approach in simulation through two experiments: a basketball throw task and a multi-step pick-and-place scenario. In the throwing task, the agent achieved a 100% success rate within a defined tolerance, with 55% of trials reaching the target with 1cm precision. In the multi-step scenario, the controller maintained high accuracy with a maximum relative error of approximately 8.5%. These results demonstrate that combining GVS-based modeling with PPO yields robust, data-efficient control policies, providing a scalable solution for controlling soft robotic systems across diverse applications.