Efficient GPU Implementation of Stable Position-Based Dynamics for Rigid Bodies
Loading...
Date
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
Highly optimized physics simulation is essential in reinforcement learning, virtual reality, and interactive simulation. Robotic reinforcement learning in particular has advanced greatly in the past few years, but recent approaches have required the simulation of tens or hundreds of thousands of agents. Several techniques have been used to improve simulation performance, including running the simulations in parallel on a graphics processing unit (GPU) and using differentiable algorithms to reduce the number of learning steps needed to converge. Previous approaches such as Isaac Gym, GRiD, and Brax have utilized some of these approaches, and many use the extended position-based dynamics (XPBD) algorithm. However, current implementations of XPBD struggle to converge to stable simulation states in certain cases. This paper describes the implementation of a GPU-optimized physics simulation system that uses a new stable and potentially differentiable version of the XPBD algorithm. Instead of constructing one large scene with all simulated agents within it, each agent is placed in its own simulation environment. This allows for fully parallel execution of each scene on the GPU, simulating tens of thousands of environments simultaneously. The new approach is orders of magnitude more performant than a central processing unit (CPU)-based implementation of this stable algorithm. Additionally, the application of GPU-specific optimization techniques such as memory layout optimization and shared memory usage further improves performance by 40-60%. In the future, this approach could be extended to include shock propagation and differentiation, or combined with a reinforcement learning framework to more efficiently train robotic reinforcement learning models.
Description
Keywords
physics simulation, optimization, CUDA, position-based dynamics, GPU acceleration, stable simulation