rail-berkeley
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README.md
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# Octo Base
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This model 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 93M parameters (equivalent to a ViT-B). 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.
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At inference, you may pass in any subset of these observation and task keys, with a history window up to 2 timesteps.
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This model was trained on a mix of datasets from the Open X-Embodiment dataset
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| Dataset | Proportion of batch |
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license: mit
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pipeline_tag: robotics
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---
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# Octo Base
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This model 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 93M parameters (equivalent to a ViT-B). 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.
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At inference, you may pass in any subset of these observation and task keys, with a history window up to 2 timesteps.
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This model was trained on a mix of datasets from the Open X-Embodiment dataset.
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| Dataset | Proportion of batch |
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