  Our goal is to develop an efficient contact detection algorithm for
large-scale GPU-based simulation of non-convex objects. Current GPU-based
simulators such as IsaacGym and Brax must trade-off speed with fidelity,
generality, or both when simulating non-convex objects. Their main issue lies
in contact detection (CD): existing CD algorithms, such as
Gilbert-Johnson-Keerthi (GJK), must trade off their computational speed with
accuracy which becomes expensive as the number of collisions among non-convex
objects increases. We propose a data-driven approach for CD, whose accuracy
depends only on the quality and quantity of offline dataset rather than online
computation time. Unlike GJK, our method inherently has a uniform computational
flow, which facilitates efficient GPU usage based on advanced compilers such as
XLA (Accelerated Linear Algebra). Further, we offer a data-efficient solution
by learning the patterns of colliding local crop object shapes, rather than
global object shapes which are harder to learn. We demonstrate our approach
improves the efficiency of existing CD methods by a factor of 5-10 for
non-convex objects with comparable accuracy. Using the previous work on contact
resolution for a neural-network-based contact detector, we integrate our CD
algorithm into the open-source GPU-based simulator, Brax, and show that we can
improve the efficiency over IsaacGym and generality over standard Brax. We
highly recommend the videos of our simulator included in the supplementary
materials.
