The script used to run the two experiments described in the paper is train_supervised.py which receives configuration files using hydra. The default configurations can be found on cfg/supervised/config.yaml. All experiments were run in an HPC using slurm. The slurm jobs are not made public but the following are some examples of how to run the experiments on standard machines

The training_supervised script will create an experiment directory on the project root folder, where the results of each individual run will be stored along with a tensorboard log file holding several metrics logged during training and validation. After training is done, each model variant is evaluated on the test dataset and a log file test_metrics.csv is created containing all evaluation metrics of the test set. These test_metrics.csv files for each of the model variants and individual seeds are used by the scripts in the paper directory to generate several figures including the ones presented in the paper.

• Atlas: Example experiment training mlp and emlp models with 256, 512, and 1024 hidden channel neurons, with and without data augmentation.

python train_supervised.py --multirun robot=Atlas dataset=com_momentum dataset.samples=500000 dataset.augment=True,False model.num_channels=256,512,1024  model=emlp,mlp dataset.train_ratio=0. model.lr=1.5e-3 model.inv_dims_sca exp_name=com_sample_eff$

• Solo with Morphological Symmetry Group C2 and K4

python train_supervised.py --multirun robot=Solo,Solo-c2 dataset=com_momentum dataset.augment=True,False dataset.train_ratio=0.70 model=emlp,mlp model.lr=2.4e-3 model.num_channels=64,128,256,512 exp_name=com_sample_eff

• CNN

python train_supervised.py --multirun robot=mini-cheetah-c2 dataset=contact dataset.data_folder=training_splitted dataset.train_ratio=0.85 dataset.augment=True,False exp_name=contact_sample_eff_splitted model=contact_cnn model.lr=1e-4 

• ECNN

python train_supervised.py --multirun robot=mini-cheetah-c2 dataset=contact dataset.data_folder=training_splitted dataset.train_ratio=0.85 dataset.augment=False exp_name=contact_sample_eff_splitted model=contact_ecnn model.lr=1e-5