Octo

This repo contains code for training and finetuning Octo generalist robotic policies (GRPs). Octo models are transformer-based diffusion policies, trained on a diverse mix of 800k robot trajectories.

Get Started

Follow the installation instructions, then load a pretrained Octo model! See examples for guides to zero-shot evaluation and finetuning and for an inference example.

from octo.model.octo_model import OctoModel
model = OctoModel.load_pretrained("hf://rail-berkeley/octo-base-1.5")
print(model.get_pretty_spec())

Out of the box, Octo supports multiple RGB camera inputs, can control various robot arms, and can be instructed via language commands or goal images. Octo uses a modular attention structure in its transformer backbone, allowing it to be effectively finetuned to robot setups with new sensory inputs, action spaces, and morphologies, using only a small target domain dataset and accessible compute budgets.

Installation

conda create -n octo python=3.10
conda activate octo
pip install -e .
pip install -r requirements.txt

For GPU:

pip install --upgrade "jax[cuda11_pip]==0.4.20" -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

For TPU

pip install --upgrade "jax[tpu]==0.4.20" -f https://storage.googleapis.com/jax-releases/libtpu_releases.html

See the Jax Github page for more details on installing Jax.

Test the installation by finetuning on the debug dataset:

python scripts/finetune.py --config.pretrained_path=hf://rail-berkeley/octo-small-1.5 --debug

Checkpoints

You can find pretrained Octo checkpoints here. At the moment we provide the following model versions:

| Model | Inference on 1x NVIDIA 4090 | Size | |---------------------------------------------------------------|-----------------------------|------------| | Octo-Base | 13 it/sec | 93M Params | | Octo-Small | 17 it/sec | 27M Params |

Examples

We provide simple example scripts that demonstrate how to use and finetune Octo models, as well as how to use our data loader independently. We provide the following examples:

| | | |----------------------------------------------------------------------|--------------------------------------------------------------------------------------------------------------------| | Octo Inference | Minimal example for loading and running a pretrained Octo model | | Octo Finetuning | Minimal example for finetuning a pretrained Octo models on a small dataset with a new observation and action space | | Octo Rollout | Run a rollout of a pretrained Octo policy in a Gym environment | | Octo Robot Eval | Evaluate a pretrained Octo model on a real WidowX robot | | OpenX Dataloader Intro | Walkthrough of the features of our Open X-Embodiment data loader | | OpenX PyTorch Dataloader | Standalone Open X-Embodiment data loader in PyTorch |

Octo Pretraining

To reproduce our Octo pretraining on 800k robot trajectories, run:

python scripts/train.py --config scripts/configs/octo_pretrain_config.py:<size> --name=octo --config.dataset_kwargs.oxe_kwargs.data_dir=... --config.dataset_kwargs.oxe_kwargs.data_mix=oxe_magic_soup ...

To download the pretraining dataset from the Open X-Embodiment Dataset, install the rlds_dataset_mod package and run the prepare_open_x.sh script. The total size of the pre-processed dataset is ~1.2TB.

We run pretraining using a TPUv4-128 pod in 8 hours for the Octo-S model and in 14 hours for Octo-B.

Octo Finetuning

We provide a minimal example for finetuning with a new observation and action space.

We also provide a more advanced finetuning script that allows you to change hyperparameters via a config file and logs finetuning metrics. To run advanced finetuning, use:

python scripts/finetune.py --config.pretrained_path=hf://rail-berkeley/octo-small-1.5

We offer three finetuning modes depending on the parts of the model that are kept frozen: head_only, head_mlp_only, and full to finetune the full model. Additionally, one can specify the task type to finetune with: image_conditioned, language_conditioned or multimodal for both. For example, to finetune the full transformer with image inputs only use: --config=finetune_config.py:full,image_conditioned.

Octo Evaluation

Loading and running a trained Octo model is as easy as:

from octo.model import OctoModel

model = OctoModel.load_pretrained("hf://rail-berkeley/octo-small-1.5")
task = model.create_tasks(texts=["pick up the spoon"])
action = model.sample_actions(observation, task, rng=jax.random.PRNGKey(0))

We provide examples for evaluating Octo in a simulated Gym environment as well as on a real WidowX robot.

To evaluate on your own environment, simply wrap it in a Gym interface and follow the instructions in the Eval Env README.

Code Structure

| | File | Description | |---------------------|---------------------------------------------------------|-------------------------------------------------------------------------------| | Hyperparameters | config.py | Defines all hyperparameters for the training run. | | Pretraining Loop | train.py | Main pretraining script. | | Finetuning Loop | finetune.py | Main finetuning script. | | Datasets | dataset.py | Functions for creating single / interleaved datasets + data augmentation. | | Tokenizers | tokenizers.py | Tokenizers that encode image / text inputs into tokens. | | Octo Model | octo_model.py | Main entry point for interacting with Octo models: loading, saving, and inference. | | Model Architecture | octo_module.py | Combines token sequencing, transformer backbone and readout heads. | | Visualization | visualization_lib.py | Utilities for offline qualitative & quantitative eval. |

FAQ

What is the timestep_pad_mask in the observation dictionary?

The timestep_pad_mask indicates which observations should be attended to, which is important when using multiple timesteps of observation history. Octo was trained with a history window size of 2, meaning the model can predict an action using both the current observation and the previous observation. However, at the very beginning of the trajectory, there is no previous observation, so we need to set timestep_pad_mask=False at the corresponding index. If you use Octo with a window size of 1, timestep_pad_mask should always just be [True], indicating that the one and only observation in the window should be attended to. Note that if you wrap your robot environment with the HistoryWrapper (see gym_wrappers.py), the timestep_pad_mask key will be added to the observation dictionary for you.

What is pad_mask_dict in the observation dictionary?

While timestep_pad_mask indicates which observations should be attended to on a timestep level, pad_mask_dict indicates which elements of the observation should be attended to within a single timestep. For example, for datasets without language labels, pad_mask_dict["language_instruction"] is set to False. For datasets without a wrist camera, pad_mask_dict["image_wrist"] is