Variational Auto-encoders factored with enforced identity of the latent variables. See reference:

Yin, H., Melo, F. S., Billard, A. and Paiva, A. Associate Latent Encodings in Learning from Demonstrations. In Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17). San Francisco, California, USA.

$ cd bin
$ ./download_data.sh
$ cd ../output
$ ./download_model.sh

Video demo

$ python vae_assoc_model_viewer.py

Launch roscore for exchanging messages.

$ roscore

In another terminal, start the the GUI and the rendering simulator

$ python baxter_openrave.py &
$ python baxter_vae_assoc_writer.py

Mandatory:

Tensorflow (>=0.8.0)

Optional (for robot motion, messaging and rendering purposes):

PyQT

ROS

OpenRAVE

baxter_pykdl