  This paper demonstrates that collision detection-intensive applications such
as robotic motion planning may be accelerated by performing collision checks
with a machine learning model. We propose Fastron, a learning-based algorithm
to model a robot's configuration space to be used as a proxy collision detector
in place of standard geometric collision checkers. We demonstrate that
leveraging the proxy collision detector results in up to an order of magnitude
faster performance in robot simulation and planning than state-of-the-art
collision detection libraries. Our results show that Fastron learns a model
more than 100 times faster than a competing C-space modeling approach, while
also providing theoretical guarantees of learning convergence. Using the OMPL
motion planning libraries, we were able to generate initial motion plans across
all experiments with varying robot and environment complexities. With Fastron,
we can repeatedly perform planning from scratch at a 56 Hz rate, showing its
application toward autonomous surgical assistance task in shared environments
with human-controlled manipulators. All performance gains were achieved despite
using only CPU-based calculations, suggesting further computational gains with
a GPU approach that can parallelize tensor algebra. Code is available online.
