SORS: A Modular, High-Fidelity Simulator for Soft Robots

2025-12-17

AI Summary

Soft robots exhibit large deformations, near-incompressibility, and complex contact interactions across multiphysics domains—posing significant modeling challenges that lead to simulation inaccuracies, numerical instability, and trade-offs between fidelity and scalability. To address these, we propose SORS: a high-fidelity, modular simulation platform for soft robotics. SORS introduces the first energy-driven, modular finite element framework, enabling user-defined constitutive laws and actuation models. It further incorporates a constraint-aware, sequential quadratic programming (SQP)-based nonlinear contact algorithm to ensure numerical stability and physical consistency. Validated via multimodal experimental calibration—including cantilever bending, pneumatic actuator deformation, and PokeFlex indentation—the platform achieves sub-millimeter deformation prediction accuracy. SORS successfully enables optimization of a soft-legged robot controller. To our knowledge, it is the first open-source soft robotics simulator that simultaneously delivers scalability, high fidelity, and usability.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

The deployment of complex soft robots in multiphysics environments requires advanced simulation frameworks that not only capture interactions between different types of material, but also translate accurately to real-world performance. Soft robots pose unique modeling challenges due to their large nonlinear deformations, material incompressibility, and contact interactions, which complicate both numerical stability and physical accuracy. Despite recent progress, robotic simulators often struggle with modeling such phenomena in a scalable and application-relevant manner. We present SORS (Soft Over Rigid Simulator), a versatile, high-fidelity simulator designed to handle these complexities for soft robot applications. Our energy-based framework, built on the finite element method, allows modular extensions, enabling the inclusion of custom-designed material and actuation models. To ensure physically consistent contact handling, we integrate a constrained nonlinear optimization based on sequential quadratic programming, allowing for stable and accurate modeling of contact phenomena. We validate our simulator through a diverse set of real-world experiments, which include cantilever deflection, pressure-actuation of a soft robotic arm, and contact interactions from the PokeFlex dataset. In addition, we showcase the potential of our framework for control optimization of a soft robotic leg. These tests confirm that our simulator can capture both fundamental material behavior and complex actuation dynamics with high physical fidelity. By bridging the sim-to-real gap in these challenging domains, our approach provides a validated tool for prototyping next-generation soft robots, filling the gap of extensibility, fidelity, and usability in the soft robotic ecosystem.

Problem

• Simulates soft robots in complex multiphysics environments with high fidelity.
• Models large nonlinear deformations, material incompressibility, and contact interactions accurately.
• Bridges the sim-to-real gap for prototyping next-generation soft robots.

Innovation

• Modular energy-based FEM framework for soft robots
• Constrained nonlinear optimization for stable contact handling
• Validated high-fidelity simulation bridging sim-to-real gap