This repository has code for our ICML 2020 paper on Learning Robot Skills with Temporal Variational Inference, authored by Tanmay Shankar and Abhinav Gupta.
Our paper presents a way to jointly learn robot skills and how to use them from demonstrations in an unsupervised manner. The code implements the training procedure for this across 3 different datasets, and provides tools to visualize the learnt skills.
Yes! If you would like to use our code, please cite our paper and this repository in your work. Also, be aware of the license for this repository: the Creative Commons Attribution-NonCommercial 4.0 International. Details may be viewed in the License file.
Great! Feel free to mail Tanmay ([email protected]), for help, suggestions, questions and feedback. You can also create issues in the repository, if you feel like the problem is pertinent to others.
You will need a few packages to be able to run the code in this repository. For Robotic environments, you will need to install Mujoco, Mujoco_Py, OpenAI Gym, and Robosuite. Here is a list of instructions on how to set these up.
You will also need some standard deep learning packages, Pytorch, Tensorflow, Tensorboard, and TensorboardX. Usually you can just pip install these packages. We recommend using a virtual environment for them.
We run our model on various publicly available datasets, i.e. the MIME dataset, the Roboturk dataset, and the CMU Mocap dataset. In the case of the MIME and Roboturk datasets, we collate relevant data modalities and store them in quickly accessible formats for our code. You can find the links to these files below.
MIME Dataset Roboturk Dataset CMU Mocap Dataset
Once you have downloaded this data locally, you will want to feed the path to these datasets in the --dataset_directory command line flag when you run your code.
Here is a list of commands to run pre-training and joint skill learning on the various datasets used in our paper. The hyper-parameters specified here are used in our paper. Depending on your use case, you may want to play with these values. For a full list of the hyper-parameters, look at Experiments/Master.py.
For the MIME dataset, to run pre-training of the low-level policy:
python Master.py --train=1 --setting=pretrain_sub --name=MIME_Pretraining --data=MIME --number_layers=8 --hidden_size=128 --kl_weight=0.01 --var_skill_length=1 --z_dimensions=64 --normalization=meanvar
This should automatically run some evaluation and visualization tools every few epochs, and you can view the results in Experimental_Logs/<Run_Name>/. Once you've run this pre-training, you can run the joint training using:
python Master.py --train=1 --setting=learntsub --name=J100 --normalization=meanvar --kl_weight=0.0001 --subpolicy_ratio=0.1 --latentpolicy_ratio=0.001 --b_probability_factor=0.01 --data=MIME --subpolicy_model=Experiment_Logs/<MIME_Pretraining>/saved_models/Model_epoch480 --latent_loss_weight=0.01 --z_dimensions=64 --traj_length=-1 --var_skill_length=1 --training_phase_size=200000
For the Roboturk dataset, to run pre-training of the low-level policy:
python Master.py --train=1 --setting=pretrain_sub --name=Roboturk_Pretraining --data=FullRoboturk --kl_weight=0.0001 --var_skill_length=1 --z_dimensions=64 --number_layers=8 --hidden_size=128
Just as in the case of the MIME dataset, you can then run the joint training using:
python Master.py --train=1 --setting=learntsub --name=RJ80 --latent_loss_weight=1. --latentpolicy_ratio=0.01 --kl_weight=0.0001 --subpolicy_ratio=0.1 --b_probability_factor=0.001 --data=Roboturk --subpolicy_model=Experiment_Logs/<Roboturk_Pretraining>/saved_models/Model_epoch20 --z_dimensions=64 --traj_length=-1 --var_skill_length=1 --number_layers=8 --hidden_size=128
Stay tuned for more!