Implementation of BBSEA from BBSEA: An Exploration of Brain-Body Synchronization for Embodied Agents.
conda create -n bbsea python=3.8
conda activate bbsea
pip install torch
git clone git@github.com:yangsizhe/CLIP.git
cd CLIP
pip install -e .
pip install -e .
export OPENAI_API_KEY=your_OPENAI_API_KEY
HYDRA_FULL_ERROR=1 CUDA_VISIBLE_DEVICES=0 python bebs_pipeline/bebs_pipeline.py max_trajectory_number_per_task=2000 success_trajectory_number_per_task=200 output_path=your_path_to_output scene_id=1
HYDRA_FULL_ERROR=1 CUDA_VISIBLE_DEVICES=0 python scalingup/train.py algo=diffusion_default evaluation.num_episodes=40 algo.replay_buffer.batch_size=256 trainer.max_epochs=1 evaluation=drawer dataset_path=your_path_to_dataset
your_path_to_dataset can be the your_path_to_output when run the pipeline to generate demonstrations.
HYDRA_FULL_ERROR=1 CUDA_VISIBLE_DEVICES=0 python scalingup/inference.py evaluation.num_episodes=10 policy=scalingup evaluation=drawer evaluation.start_episode=100000 policy.path=/path/to/your/checkpoint.ckpt
BBSEA is licensed under the MIT license. MuJoCo is licensed under the Apache 2.0 license.
We utilize the official implementation of scalingup as codebase.
If you find our work useful, please consider citing:
@article{yang2024bbsea,
title={BBSEA: An Exploration of Brain-Body Synchronization for Embodied Agents},
author={Yang, Sizhe and Luo, Qian and Pani, Anumpam and Yang, Yanchao},
journal={arXiv preprint arXiv:2402.08212},
year={2024}
}