Differentiable Collision Detection for a Set of Convex Primitives
Kevin Tracy,Taylor A. Howell,Zachary Manchester,Kevin Tracy,Taylor A. Howell,Zachary Manchester
Collision detection between objects is critical for simulation, control, and learning for robotic systems. How-ever, existing collision detection routines are inherently non-differentiable, limiting their applications in gradient-based opti-mization tools. In this work, we propose DCOL: a fast and fully differentiable collision-detection framework that reasons about collisions between a set of com...
