  Effective motion planning in high dimensional spaces is a long-standing open
problem in robotics. One class of traditional motion planning algorithms
corresponds to potential-based motion planning. An advantage of potential based
motion planning is composability -- different motion constraints can be easily
combined by adding corresponding potentials. However, constructing motion paths
from potentials requires solving a global optimization across configuration
space potential landscape, which is often prone to local minima. We propose a
new approach towards learning potential based motion planning, where we train a
neural network to capture and learn an easily optimizable potentials over
motion planning trajectories. We illustrate the effectiveness of such approach,
significantly outperforming both classical and recent learned motion planning
approaches and avoiding issues with local minima. We further illustrate its
inherent composability, enabling us to generalize to a multitude of different
motion constraints.
