  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 to
continuously represent arbitrary motions parameterized by their start and goal
configurations. This allows to quickly compute signed distances for any point
in the task space to the robot motion. Further, we present an algorithm
combining the speed of the deep learning-based signed distance computations
with the strong accuracy guarantees of geometric collision checkers. We
validate our approach in simulated and real-world robotic experiments, and
demonstrate that it is able to speed up a commercial bin picking application.
