OpenVLA-7B
| Property | Value |
|---|---|
| Parameter Count | 7.54B |
| License | MIT |
| Paper | OpenVLA: An Open-Source Vision-Language-Action Model |
| Architecture | Vision-Language-Action Model (Based on DINOv2 ViT-L/14 and Llama-2) |
What is OpenVLA-7B?
OpenVLA-7B is a groundbreaking vision-language-action model developed by researchers from Stanford, UC Berkeley, Google Deepmind, and Toyota Research Institute. It's designed to bridge the gap between visual perception, language understanding, and robotic action generation. Trained on 970,000 robot manipulation episodes from the Open X-Embodiment dataset, this model can interpret natural language instructions alongside visual input to generate precise robot control actions.
Implementation Details
The model is built on a sophisticated architecture combining DINOv2 ViT-L/14 and SigLIP ViT-So400M/14 for vision processing, integrated with Llama-2 for language understanding. It processes inputs in BF16 format and generates 7-DoF end-effector deltas (x, y, z, roll, pitch, yaw, gripper) as output.
- Unified vision-language-action processing pipeline
- Zero-shot control capability for supported robot configurations
- Parameter-efficient fine-tuning support for new domains
- Built on proven foundation models (DINOv2, Llama-2)
Core Capabilities
- Direct robot control through natural language instructions
- Multi-robot support out-of-the-box
- Real-time visual scene understanding and action generation
- Efficient adaptation to new robot domains via fine-tuning
Frequently Asked Questions
Q: What makes this model unique?
OpenVLA-7B stands out for its ability to directly translate visual and language inputs into robot actions, trained on one of the largest datasets of robot manipulation episodes. Its zero-shot capabilities and efficient fine-tuning options make it particularly valuable for robotics applications.
Q: What are the recommended use cases?
The model is ideal for robot control scenarios where visual feedback and natural language instructions need to be processed together, particularly in manipulation tasks. It's especially suited for BridgeV2 environments with Widow-X robots, though it can be adapted to other setups through fine-tuning.
