GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients
This provides a performance boost for robotics researchers and engineers in trajectory optimization, though it is incremental as it builds on existing methods with GPU acceleration.
The authors tackled the problem of accelerating rigid body dynamics computations for robotic planning and control by introducing GRiD, a GPU-accelerated library with analytical gradients, achieving up to a 7.2x speedup over a state-of-the-art CPU implementation.
We introduce GRiD: a GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate the nonlinear trajectory optimization subproblem used in state-of-the-art robotic planning, control, and machine learning, which requires tens to hundreds of naturally parallel computations of rigid body dynamics and their gradients at each iteration. GRiD leverages URDF parsing and code generation to deliver optimized dynamics kernels that not only expose GPU-friendly computational patterns, but also take advantage of both fine-grained parallelism within each computation and coarse-grained parallelism between computations. Through this approach, when performing multiple computations of rigid body dynamics algorithms, GRiD provides as much as a 7.2x speedup over a state-of-the-art, multi-threaded CPU implementation, and maintains as much as a 2.5x speedup when accounting for I/O overhead. We release GRiD as an open-source library for use by the wider robotics community.