This is the Diffusion Policy fork repo for running RoboCasa benchmark experiments. This fork is based on the original Diffusion Policy code, hosted at https://github.com/real-stanford/diffusion_policy.
For training we recommend a GPU with at least 24 Gb of memory, but 48 Gb+ is prefered. For inference we recommend a GPU with at least 8 Gb of memory.
git clone https://github.com/robocasa-benchmark/diffusion_policy
cd diffusion_policy
pip install -e .
- Training: train.py
- Evaluation: eval_robocasa.py
# train model
python train.py \
--config-name=train_diffusion_transformer_bs192 \
task=robocasa/<dataset-soup>
# Evaluate model
python eval_robocasa.py \
--checkpoint <checkpoint-path> \
--task_set <task-set> \
--split <split>
# Report evaluation results
python diffusion_policy/scripts/get_eval_stats.py \
--dir <outputs-dir>