The repository follows along with the paper "Learning Sampling Distributions for Robot Motion Planning" by Brian Ichter, James Harrison, and Marco Pavone of Stanford's Autonomous Systems Lab, submitted to ICRA 2018.
The iPython notebook learns a sampling distribution for a narrow passage problem from the included data set. The distribution is computed through a conditional variational autoencoder, where samples are generated by sampling the latent space conditioned on the problem's initial state, goal state, and an occupancy grid of the obstacles.
Planning code to run the sample sets or generate data can be found at https://github.com/StanfordASL/GMT.
Copyright 2019 Brian Ichter
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