Physically Accurate Rigid-Body Dynamics in Particle-Based Simulation
This addresses the need for physically accurate simulations in robotics, enabling better reasoning about diverse real-world interactions, though it is incremental as it builds upon existing PBD methods.
The paper tackled the problem of physical inaccuracy in particle-based simulators like position-based dynamics (PBD) for robotics by introducing PBD-R, a revised formulation with momentum-conservation constraints and modified velocity updates, which significantly outperformed PBD and achieved competitive accuracy with MuJoCo while requiring less computation.
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.