Motion Planning Networks: Bridging the Gap Between Learning-based and Classical Motion Planners
This work addresses motion planning for robotics by bridging learning-based and classical approaches, offering a hybrid method with computational and optimality gains, though it is incremental in combining existing techniques.
The paper tackles motion planning problems by introducing Motion Planning Networks (MPNet), a learning-based neural planner that efficiently generates near-optimal paths in seen and unseen environments, achieving significant performance improvements over standard methods in tests from 2D to 7D robot spaces.
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.