Differentiable Collision Detection

Differentiable Collision Detection

Collision detection between objects is critical for simulation, control, and learning for robotic systems. However, existing collision detection routines are inherently non-differentiable, limiting their usefulness in optimization-based algorithms. In this work, we propose a fully differentiable collision-detection framework that reasons about distances between a set of composable and highly expressive convex primitive shapes. This is achieved by formulating the collision detection problem as a convex optimization problem that seeks to find the minimum uniform scaling to be applied to each object before there is an intersection. The optimization problem is fully differentiable and is able to return both the collision detection status as well as the contact points on each object.

Related Papers

People

Kevin Tracy
Optimization and control
Taylor Howell
Model-predictive control and differentiable simulators for contact
Simon Le Cleac'h
Game-theoretic optimization and optimization through contact
Zac Manchester
Assistant Professor
Last updated: 2022-08-10