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Mini GRP: Mini- Generalist Robotics Policy

Minimialist reimplimentation of the Octo Generalist Robotics Policy.

Install

'''module load cudatoolkit/11.8 miniconda/3'''

conda create -n mini-grp python=3.10 conda activate mini-grp pip install -r requirements.txt conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia

pip install torch==2.4.0 pip install hydra-submitit-launcher --upgrade

Install MilaTools

pip install milatools==0.1.14 decorator==4.4.2 moviepy==1.0.3

Dataset

https://rail-berkeley.github.io/bridgedata/

Install SimpleEnv

Prerequisites:

CUDA version >=11.8 (this is required if you want to perform a full installation of this repo and perform RT-1 or Octo inference)
An NVIDIA GPU (ideally RTX; for non-RTX GPUs, such as 1080Ti and A100, environments that involve ray tracing will be slow). Currently TPU is not supported as SAPIEN requires a GPU to run.

Clone this repo:

git clone https://github.com/simpler-env/SimplerEnv --recurse-submodules

Install numpy<2.0 (otherwise errors in IK might occur in pinocchio):

pip install numpy==1.24.4

Install ManiSkill2 real-to-sim environments and their dependencies:

cd {this_repo}/ManiSkill2_real2sim
pip install -e .

Install this package:

cd {this_repo}
pip install -e .

conda install conda-forge::vulkan-tools conda-forge::vulkan-headers

Running the code

Basic example to train the GRP over the bridge dataset

python mini-grp.py

Launch multiple jobs on a slurm cluster to evalute different model architectures, etc.

python mini-grp.py --multirun gradient_accumulation_steps=1,2,4 hydra/launcher=submitit_slurm

License

MIT

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