Motion planning networks: Bridging the gap between learning-based and classical motion planners

IEEE Transactions on Robotics, 2020

Ahmed Hussain Qureshi, Yinglong Miao, Anthony Simeonov, Michael C Yip

Publisher Link: http://ieeexplore.ieee.org/abstract/document/9154607/

Abstract: This article 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 …

Qureshi et al. (2020) Motion planning networks: Bridging the gap between learning-based and classical motion planners, IEEE Transactions on Robotics, vol. 37, no. 1, pp. 48-66.