Foundation Models

OpenVLA

OpenVLA is an open-source 7-billion-parameter vision-language-action model released in 2024 by a Stanford-led team, built on a Llama 2 language backbone with fused SigLIP and DINOv2 visual features and trained on about 970,000 robot episodes from Open X-Embodiment. It outputs discretized actions autoregressively, reported stronger results than the much larger closed RT-2-X on evaluated tasks, and supports efficient LoRA fine-tuning to new robots.

Why it matters for physical AI

As the first widely adopted fully open VLA with released weights and training code, it became a standard base model for fine-tuning and a reference point for subsequent open robot foundation models.