SoftManiSim: A Fast Simulation Framework for Multi-Segment Continuum Manipulators Tailored for Robot Learning

Abstract

This paper introduces SoftManiSim, a novel simulation framework for multi-segment continuum manipulators. Existing continuum robot simulators often rely on simplifying assumptions, such as constant curvature bending or ignoring contact forces, to meet real-time simulation and training demands. To bridge this gap, we propose a robust and rapid mathematical model for continuum robots at the core of SoftManiSim, ensuring precise and adaptable simulations. The framework can integrate with various rigid-body robots, increasing its utility across different robotic platforms. SoftManiSim supports parallel operations for simultaneous simulations of multiple robots and generates synthetic data essential for training deep reinforcement learning models. This capability enhances the development and optimization of control strategies in dynamic environments. Extensive simulations validate the framework's effectiveness, demonstrating its capabilities in handling complex robotic interactions and tasks. We also present real robot validation to showcase the simulator's practical applicability and accuracy in real-world settings. To our knowledge, SoftManiSim is the first open-source real-time simulator capable of modeling continuum robot behavior under dynamic point/distributed loading. It enables rapid deployment in reinforcement learning and machine learning applications. This simulation framework can be downloaded from https://github.com/MohammadKasaei/SoftManiSim.