ComFree-Sim: A GPU-Parallelized Analytical Contact Physics Engine for Scalable Contact-Rich Robotics Simulation and Control

2026-03-12

AI Summary

This work addresses the computational bottleneck in contact-intensive robotic simulation, where conventional physics engines suffer from superlinear scaling in contact count due to reliance on complementarity constraints or optimization-based methods, hindering high-frequency control. The authors propose a complementarity-free analytical contact engine that leverages an impedance-inspired predictor-corrector scheme within the Coulomb friction dual cone to compute contact impulses in closed form, fully decoupling contact pairs and enabling GPU parallelization. The method unifies tangential, torsional, and rolling friction into a separable 6D contact model and is efficiently accelerated via Warp, offering a plug-and-play backend compatible with MuJoCo. Experiments demonstrate near-linear runtime scaling in dense-contact scenarios, achieving 2–3× higher throughput than existing simulators while maintaining physical fidelity comparable to MJX, and enabling real-time model predictive control and dexterous manipulation with significantly improved closed-loop success rates.

Technology Category

• Computer Vision: Low Level & Physics-based Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Physics simulation for contact-rich robotics is often bottlenecked by contact resolution: mainstream engines enforce non-penetration and Coulomb friction via complementarity constraints or constrained optimization, requiring per-step iterative solves whose cost grows superlinearly with contact density. We present ComFree-Sim, a GPU-parallelized analytical contact physics engine built on complementarity-free contact modeling. ComFree-Sim computes contact impulses in closed form via an impedance-style prediction--correction update in the dual cone of Coulomb friction. Contact computation decouples across contact pairs and becomes separable across cone facets, mapping naturally to GPU kernels and yielding near-linear runtime scaling with the number of contacts. We further extend the formulation to a unified 6D contact model capturing tangential, torsional, and rolling friction, and introduce a practical dual-cone impedance heuristic. ComFree-Sim is implemented in Warp and exposed through a MuJoCo-compatible interface as a drop-in backend alternative to MuJoCo Warp (MJWarp). Experiments benchmark penetration, friction behaviors, stability, and simulation runtime scaling against MJWarp, demonstrating near-linear scaling and 2--3 times higher throughput in dense contact scenes with comparable physical fidelity. We deploy ComFree-Sim in real-time MPC for in-hand dexterous manipulation on a real-world multi-fingered LEAP hand and in dynamics-aware motion retargeting, demonstrating that low-latency simulation yields higher closed-loop success rates and enables practical high-frequency control in contact-rich tasks.

Problem

• contact-rich robotics
• physics simulation
• contact resolution
• computational scalability
• real-time control

Innovation

• complementarity-free contact
• GPU-parallelized physics simulation
• analytical contact impulse
• dual-cone impedance
• 6D friction model