Model-Predictive Motion Planning Networks

Model Predictive Motion Planning Network (MPC-MPNet) is a neural network-based kinodynamic planning algorithm capable of solving Kinodynamic Motion Planning tasks in seconds with less variability and comparable path quality against a state-of-the-art algorithm that takes minutes to generate solutions with high variability. MPC-MPNet utilize CNN-based neural network to learn environment embedding and predict waypoints satisfying kinodynamic constrains. Our experiments show that MPC-MPNet generalizes to unseen tasks and adapts to high-dimensional problems such as Quadrotor with high success rate and high-quality solutions. MPC-MPNet is fully parallelizable and available for GPU computation, achieving practical completeness and optimality. Our algorithms show theoretical asymptotic optimality when SST is used as a backup planner. Moreover, MPC-MPNet provides a generalized framework for the KMP planner, where multiple variants can easily be adapted.

Source Code & Supplementary Material

1. Paper

2. Repository of MPC-MPNet

3. Video

Motion Planning with MPC-MPNet in Cluttered Environments with Underactuated Dynamics

We evaluate MPC-MPNet to plan motion for four underactuated dynamic systems: Acrobot, Cartpole, Car and Quadrotor. The environments are random generated and the start and goal states are unseen during training.

Acrobot

Cartpole

Car

Quadrotor