GRiD: GPU-Accelerated Rigid Body Dynamics with Analytical Gradients

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 natu...

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Bibliographic Details
Published inarXiv.org
Main Authors Plancher, Brian, Neuman, Sabrina M, Ghosal, Radhika, Kuindersma, Scott, Reddi, Vijay Janapa
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 25.02.2022
Subjects
Algorithms
Computer Science - Robotics
Iterative methods
Libraries
Machine learning
Rigid structures
Rigid-body dynamics
Robot control
Robotics
Trajectory optimization
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Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2109.06976