OpenVLA Collaboration

Model · Robotics

openvla-7b

OpenVLA Collaboration

OpenVLA 7B (openvla-7b) is an open vision-language-action model trained on 970K robot manipulation episodes from the Open X-Embodiment dataset. The model takes language instructions and camera images as input and generates robot actions. It supports controlling multiple robots out-of-the-box, and can be quickly adapted for new robot domains via (parameter-efficient) fine-tuning. All OpenVLA checkpoints, as well as our training codebase are released under an MIT License. For full details, please read our paper and see our project page. OpenVLA models take a language instruction and a camera image of a robot workspace as input, and predict (normalized) robot actions consisting of 7-DoF…

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openvla-7b-finetuned-libero-spatial

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-Spatial dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.

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openvla-7b-finetuned-libero-object

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-Object dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.

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openvla-7b-finetuned-libero-10

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-10 (LIBERO-Long) dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.

Model · Image and text to text

openvla-7b-finetuned-libero-goal

OpenVLA Collaboration

This model was produced by fine-tuning the OpenVLA 7B model via LoRA (r=32) on the LIBERO-Goal dataset from the LIBERO simulation benchmark. We made a few modifications to the training dataset to improve final performance (see the OpenVLA paper for details). Below are the hyperparameters we used for all LIBERO experiments: - No gradient accumulation (i.e. gradaccumulationsteps == 1) - shufflebuffersize == 100000 See the OpenVLA GitHub README for instructions on how to run and evaluate this model in the LIBERO simulator.