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SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation <br/> ICML 2024

@inproceedings{2024sam,
         author    = {Junjie Zhang, Chenjia Bai, Haoran He, Zhigang Wang, Bin Zhao, Xiu Li, Xuelong Li},
         title     = {SAM-E: Leveraging Visual Foundation Model with Sequence Imitation for Embodied Manipulation},
         booktitle = {International Conference on Machine Learning}
         year      = {2024},
}

This is the offical repository for SAM-E. Currently we provide the code for training. More updates are coming soon.

Get Started

Install

Step 1: create a conda environment

conda create --name samE python=3.8
conda activate samE

Step 2: Install PyTorch compatible with your CUDA version.
Step 3: Install PyTorch3D.

PyTorch3D is essential for our SAM-E.

One recommended version that is compatible with the rest of the library can be installed as follows. Note that this might take some time. For more instructions visit here.

curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
tar xzf 1.10.0.tar.gz
export CUB_HOME=$(pwd)/cub-1.10.0
pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'

Step 4: Install CoppeliaSim. PyRep requires version 4.1 of CoppeliaSim. Download and unzip CoppeliaSim:
Ubuntu 16.04
Ubuntu 18.04
Ubuntu 20.04

Once you have downloaded CoppeliaSim, add the following to your ~/.bashrc file. (NOTE: the 'EDIT ME' in the first line)

export COPPELIASIM_ROOT=<EDIT ME>/PATH/TO/COPPELIASIM/INSTALL/DIR
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$COPPELIASIM_ROOT
export QT_QPA_PLATFORM_PLUGIN_PATH=$COPPELIASIM_ROOT
export DISPLAY=:1.0

Remember to source your .bashrc (source ~/.bashrc) or .zshrc (source ~/.zshrc) after this.

Step 5: Install samE and other submodules using the following command.

cd SAM-E/
pip install -e . 
pip install -e samE/libs/PyRep 
pip install -e samE/libs/RLBench 
pip install -e samE/libs/YARR 
pip install -e samE/libs/peract_colab

Training

Step 1: Download dataset
- We use the same dataset as RVT for experiments in RLBench. Download the dataset provided by PerAct to SAM-E/samE/data/. For training, you may only need to download the /train/ and take it as SAM-E/samE/data/train/.
Step 2: Start training

python train.py --exp_cfg_path configs/samE.yaml --mvt_cfg_path configs/mvt_samE.yaml --device 0,1,2,3

Change the device flag depending on available gpus.

Evaluation

We evaluate the model on RLBench. First download the eval dataset and move the /eval/ to SAM-E/samE/data/eval/.

After training, you will have the checkpoint and configs at SAM-E/samE/runs/. Run the evaluation by:

python eval.py  --model-folder runs/sam_e --eval-datafolder ./data/test --tasks all --eval-episodes 25 --log-name test/final --device 0 --model-name model_14.pth

Checkpoint

We provide a checkpoint of the multi-task training which is available here.

Acknowledgement

We sincerely thank the authors of the following repositories for sharing their code.