Title: Motion Planning Networks,
Abstract: Fast and efficient motion planning algorithms are crucial for many
state-of-the-art robotics applications such as self-driving cars. Existing
motion planning methods such as RRT*, A*, and D*, become ineffective as their
computational complexity increases exponentially with the dimensionality of the
motion planning problem. To address this issue, we present a neural
network-based novel planning algorithm which generates end-to-end
collision-free paths irrespective of the obstacles' geometry. The proposed
method, called MPNet (Motion Planning Network), comprises of a Contractive
Autoencoder which encodes the given workspaces directly from a point cloud
measurement, and a deep feedforward neural network which takes the workspace
encoding, start and goal configuration, and generates end-to-end feasible
motion trajectories for the robot to follow. We evaluate MPNet on multiple
planning problems such as planning of a point-mass robot, rigid-body, and 7 DOF
Baxter robot manipulators in various 2D and 3D environments. The results show
that MPNet is not only consistently computationally efficient in all 2D and 3D
environments but also show remarkable generalization to completely unseen
environments. The results also show that computation time of MPNet consistently
remains less than 1 second which is significantly lower than existing
state-of-the-art motion planning algorithms. Furthermore, through transfer
learning, the MPNet trained in one scenario (e.g., indoor living places) can
also quickly adapt to new scenarios (e.g., factory floors) with a little amount
of data.