  This paper describes Motion Planning Networks (MPNet), a computationally
efficient, learning-based neural planner for solving motion planning problems.
MPNet uses neural networks to learn general near-optimal heuristics for path
planning in seen and unseen environments. It takes environment information such
as raw point-cloud from depth sensors, as well as a robot's initial and desired
goal configurations and recursively calls itself to bidirectionally generate
connectable paths. In addition to finding directly connectable and near-optimal
paths in a single pass, we show that worst-case theoretical guarantees can be
proven if we merge this neural network strategy with classical sample-based
planners in a hybrid approach while still retaining significant computational
and optimality improvements. To train the MPNet models, we present an active
continual learning approach that enables MPNet to learn from streaming data and
actively ask for expert demonstrations when needed, drastically reducing data
for training. We validate MPNet against gold-standard and state-of-the-art
planning methods in a variety of problems from 2D to 7D robot configuration
spaces in challenging and cluttered environments, with results showing
significant and consistently stronger performance metrics, and motivating
neural planning in general as a modern strategy for solving motion planning
problems efficiently.
