Neural Implicit Swept Volume Models for Fast Collision Detection

arXiv:2402.15281v1 Announce Type: cross
Abstract: Collision detection is one of the most time-consuming operations during motion planning. Thus, there is an increasing interest in exploring machine learning techniques to speed up collision detection and sampling-based motion planning. A recent line of research focuses on utilizing neural signed distance functions of either the robot geometry or the swept volume of the robot motion. Building on this, we present a novel neural implicit swept volume model that is the first to continuously …

abstract arxiv collision cs.lg cs.ro detection functions line machine machine learning machine learning techniques motion planning operations planning research sampling speed type