Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation

2026-03-15

AI Summary

Existing particle-based simulation methods often lack sufficient physical accuracy for robotic applications, failing to meet the demands of high-fidelity rigid-body dynamics. This work proposes PBD-R, an enhanced position-based dynamics (PBD) approach that, for the first time within the PBD framework, incorporates momentum-conserving constraints and introduces a corrected velocity update scheme. These innovations enable efficient and physically accurate rigid-body simulation under a unified particle representation. A solver-agnostic evaluation benchmark demonstrates that PBD-R achieves physical accuracy comparable to MuJoCo and substantially surpasses conventional PBD, all while maintaining lower computational overhead.

Technology Category

• Intelligent Robots: Motion and Path Planning
• Computer Vision: Low Level & Physics-based Vision
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Robotics demands simulation that can reason about the diversity of real-world physical interactions, from rigid to deformable objects and fluids. Current simulators address this by stitching together multiple subsolvers for different material types, resulting in a compositional architecture that complicates physical reasoning. Particle-based simulators offer a compelling alternative, representing all materials through a single unified formulation that enables seamless cross-material interactions. Among particle-based simulators, position-based dynamics (PBD) is a popular solver known for its computational efficiency and visual plausibility. However, its lack of physical accuracy has limited its adoption in robotics. To leverage the benefits of particle-based solvers while meeting the physical fidelity demands of robotics, we introduce PBD-R, a revised PBD formulation that enforces physically accurate rigid-body dynamics through a novel momentum-conservation constraint and a modified velocity update. Additionally, we introduce a solver-agnostic benchmark with analytical solutions to evaluate physical accuracy. Using this benchmark, we show that PBD-R significantly outperforms PBD and achieves competitive accuracy with MuJoCo while requiring less computation.

Problem

• rigid-body dynamics
• physical accuracy
• particle-based simulation
• position-based dynamics
• robotics simulation

Innovation

• position-based dynamics
• rigid-body dynamics
• momentum conservation
• particle-based simulation
• physical accuracy