Octo Small
See https://github.com/octo-models/octo for instructions for using this model.
Octo Small is trained with a window size of 2, predicting 7-dimensional actions 4 steps into the future using a diffusion policy. The model is a Transformer with 27M parameters (equivalent to a ViT-S). Images are tokenized by preprocessing with a lightweight convolutional encoder, then grouped into 16x16 patches. Language is tokenized by applying the T5 tokenizer, and then applying the T5-Base language encoder.
Observations and tasks conform to the following spec:
Observations:
{
image_primary: ('batch', 'history_window', 256, 256, 3),
image_wrist: ('batch', 'history_window', 128, 128, 3),
}
Tasks:
{
image_primary: ('batch', 256, 256, 3),
image_wrist: ('batch', 128, 128, 3),
language_instruction: {
attention_mask: ('batch', 16),
input_ids: ('batch', 16),
},
}
At inference, you may pass in any subset of these observation and task keys, with a history window up to 2 timesteps.
This model was trained on a mix of datasets from the Open X-Embodiment dataset.
| Dataset | Proportion of batch |
|---|---|
| Fractal (Brohan et al, 2022) | 17.0% |
| Kuka (Kalashnikov et al, 2018) | 17.0% |
| Bridge (Walke et al, 2023) | 17.0% |
| BC-Z (Jang et al, 2022) | 9.1% |
| Stanford Hydra Dataset (Belkhale et al, 2023) | 6.0% |
| Language Table~ (Lynch et al, 2023) | 5.9% |
| Taco Play (Rosete-Beas et al, 2022, Mees et al., 2023) | 3.6% |
| Furniture Bench Dataset (Heo et al, 2023) | 3.3% |
| UTAustin Mutex (Shah et al, 2023) | 3.0% |
| Austin Sailor Dataset (Nasiriany et al, 2022) | 2.9% |
| Roboturk (Mandlekar et al, 2018) | 2.8% |
| Toto (Zhou et al, 2023) | 2.4% |
| Austin Sirius Dataset (Liu et al, 2023) | 2.3% |
| Berkeley Autolab UR5 (Chen et al) | 1.5% |
| IAMLab CMU Pickup Insert (Saxena et al, 2023) | 1.2% |
| Viola (Zhu et al, 2023) | 1.2% |
| Berkeley Fanuc Manipulation (Zhu et al, 2023) | 1.0% |
| NYU Franka Play Dataset (Cui et al, 2022) | 0.9% |
| UCSD Kitchen Dataset (Ge Yan and Wang, 2023) | <0.1% |
| Jaco Play (Dass et al, 2023) | 0.6% |
| Berkeley Cable Routing (Luo et al, 2023) | 0.3% |
| Austin Buds Dataset (Zhu et al, 2022) | 0.3% |
| CMU Stretch (Mendonca et al, 2023) | 0.2% |
| NYU Door Opening (Pari et al, 2021) | 0.1% |
| DLR EDAN Shared Control (Quere et al, 2020) | 0.1% |
Updates for Version 1.5
- Language task tokens are now repeated at every timestep in the context window.
- Augmented the language instructions in the data with rephrasings from GPT-3.5.
- Bug fixes:
- Turned off dropout in the diffusion head due to incompatibility with layer norm.
- Fixed an off-by-one error with the attention mask.
- Fixed an issue where different image augmentations did not get fresh random seeds.