OpenVLA

OpenVLA is an open source vision language action (VLA) model with 700 million parameters, which is pre trained through 970k robot dramas on the Open X-Embodiment dataset. This model sets a new industry standard on the general robot operation strategy, supports the control of multiple robots out of the box, and can quickly adapt to new robot settings through efficient fine-tuning of parameters. The OpenVLA checkpoint and PyTorch training process are completely open source, and the model can be downloaded from HuggingFace and fine tuned.

Demand group:

"The OpenVLA model is mainly aimed at robot researchers and developers, especially those who need to rapidly deploy and adapt to a variety of robot operation tasks. Its open source features and efficient fine-tuning capabilities enable researchers and engineers to easily apply the model to different robot platforms and operation scenarios."

Example usage scenarios:

Use OpenVLA to control Franka Panda robot to place objects on the desktop.

OpenVLA is deployed on the WidowX robot to perform complex object operations and environment interactions.

OpenVLA is applied on Google robot to realize object operation based on natural language instructions.

Product features:

Support the control of multiple robot platforms without additional training.

Adapt quickly to new robot settings through efficient fine tuning of parameters.

Excellent performance in visual, motor, physical and semantic generalization tasks.

Primatic-7B VLM is used for pre training, including fusion visual encoder, projector and Llama 27B language model.

In a multi task and multi object environment, language instructions are effectively combined with behaviors.

The LoRA technology enables efficient parameter tuning, with only 1.4% of the parameters being fine tuned.

Tutorial:

1. Visit the HuggingFace website to download the checkpoints of the OpenVLA model.

2. Set up the PyTorch training environment to ensure that all dependencies are correctly installed.

3. Fine tune OpenVLA according to specific robot platform and task requirements.

4. Use LoRA technology or other parameter efficient methods to optimize model performance.

5. Deploy the fine tuned model on the robot and conduct actual operation test.

6. According to the test results, further adjust the model parameters to adapt to more complex operation tasks.

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