Final Course Project for CSC2626: Imitation Learning for Robotics

Graph Imitation Learning

The key codebase is in src/graphs, a list of available arguments can be found in src/graphs/config.py.

Installation

We recommend using Anaconda to install all relevant packages:

1. Create a new environment (Python 3.8 or older only)

conda create -n graphs python=3.8.8

2. Activate the environment:

conda activate graphs

3. Install PyTorch from https://pytorch.org/

conda install pytorch torchvision torchaudio cudatoolkit=11.1 -c pytorch -c=conda-forge

4. Install PyTorch Geometric https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html (Make sure you are running the anaconda prompt as administrator)

pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-sparse -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-cluster -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-${TORCH}+${CUDA}.html
pip install torch-geometric

5. Install the rest of the packages

pip install -r requirements.txt

Training

To train MLP baseline for Reach-Target, with imitation dataset in data/reach_target, use

python src/graphs/reach_target_mlp.py --data_dir data/reach_target  --model_name mlp --hidden_dims 64 64 64 --num_epochs 3000 --lr 0.0001 --eval_when_train --tag 64x3_lr --seed 9

To train MLP baseline for Pick-and-Lift, with imitation dataset in data/pick_and_lift, use

python src/graphs/pick_and_lift_mlp.py --data_dir data/pick_and_lift  --model_name mlp --hidden_dims 64 64 64 --num_epochs 3000 --lr 0.0001 --eval_when_train --tag 64x3_lr --seed 9

Evaluation

To generate evaluation rollouts for a given checkpoint, for example, rt_mlp_64x3_lr/seed6_Apr15_14-04-30, use

python src/graphs/reach_target_mlp.py --eval --eval_batch_size 10 --max_episode_length 250 --checkpoint_dir logs/rt_mlp_64x3_lr/seed6_Apr15_14-04-30/ --render

Visualization

1. To visualize the resulting model's loss in Tensorboard, run the following command, pointing to the logs directory where the experiments are saved: tensorboard --logdir=logs

2. Then, go to localhost:6006 in your browser to view Tensorboard.