Rigid Body Dynamics

GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients

We introduce and release GRiD, an open-source, GPU-accelerated library for computing rigid body dynamics with analytical gradients. GRiD was designed to accelerate nonlinear trajectory optimization Through optimized code generation, GRiD provides as much as a 7.6x speedup over a state-of-the-art, multi-threaded CPU implementation and maintains as much as a 2.6x speedup when accounting for I/O overhead.

Accelerating Robot Dynamics Gradients on a CPU, GPU, and FPGA

In this paper, we detail the designs of three faster than state-of-the-art implementations of the gradient of rigid body dynamics on a CPU, GPU, and FPGA. Our optimized FPGA and GPU implementations provide as much as a 3.0x end-to-end speedup over our optimized CPU implementation by refactoring the algorithm to exploit its computational features, e.g., parallelism at different granularities.