Foundation Models

Octo

Octo is an open-source generalist robot policy released by a UC Berkeley-led team in 2024, trained on roughly 800,000 trajectories from the Open X-Embodiment dataset. It uses a transformer backbone with a diffusion action head, supports language and goal-image conditioning, and is designed to be fine-tuned to new robots, sensors, and action spaces with modest data and compute.

Why it matters for physical AI

Octo demonstrated that mid-sized open models pretrained on cross-embodiment data can be adapted cheaply to new platforms, making it a common baseline and starting point for policy fine-tuning.