  Reliable real-time planning for robots is essential in today's rapidly
expanding automated ecosystem. In such environments, traditional methods that
plan by relaxing constraints become unreliable or slow-down for kinematically
constrained robots. This paper describes the algorithm Dynamic Motion Planning
Networks (Dynamic MPNet), an extension to Motion Planning Networks, for
non-holonomic robots that address the challenge of real-time motion planning
using a neural planning approach. We propose modifications to the training and
planning networks that make it possible for real-time planning while improving
the data efficiency of training and trained models' generalizability. We
evaluate our model in simulation for planning tasks for a non-holonomic robot.
We also demonstrate experimental results for an indoor navigation task using a
Dubins car.
