The following instructions were tested on Ubuntu 22.04 and Linux 5.13.0 using Python 3.10
Create a virtual environment using Anaconda:
conda create -n robot-learning python=3.10
conda activate robot-learning
Clone and install Octo:
git clone https://github.com/octo-models/octo.git
cd octo
pip install -e .
pip install -r requirements.txt
Install JAX (GPU or TPU):
pip install --upgrade "jax[cuda11_pip]==0.4.20" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
pip install --upgrade "jax[tpu]==0.4.20" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html
Clone Tony Z. Zhao's ACT (Action Chunking with Transformers) for its simulated ALOHA environment Transfer Cube:
git clone https://github.com/tonyzhaozh/act.git
pip3 install opencv-python modern_robotics pyrealsense2 h5py_cache pyquaternion pyyaml rospkg pexpect mujoco==2.3.3 dm_control==1.0.9 einops packaging h5py ipython
Finetuning Octo to a new observation space and a new action space on simulated ALOHA cube handover data.
Download and extract the dataset:
wget https://rail.eecs.berkeley.edu/datasets/example_sim_data.zip
unzip example_sim_data.zip
Run scripts/finetune.py:
python scripts/finetune.py \
--pretrained_path=hf://rail-berkeley/octo-small \
--data_dir=PATH/TO/aloha_sim_dataset \
--save_dir=PATH/TO/CHECKPOINT/DIR
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