octo-models/octo

Health v2 · maintenance only

Adoption not part of the score

1751 stars · 281 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Octo is an open-source generalist robot policy: a transformer-based diffusion policy pretrained on 800k robot trajectories from the Open X-Embodiment dataset. It provides pretrained checkpoints (Octo-Small and Octo-Base) plus training and finetuning code built on JAX for adapting the policy to new robots, sensors, and action spaces.

Use cases

• control a robot arm with a pretrained generalist policy
• finetune a robot policy to my custom robot setup with a small dataset
• run zero-shot evaluation of a robot manipulation model
• instruct a robot via language commands or goal images
• research generalist robot policies and multi-embodiment learning
• load a pretrained robot policy checkpoint from Hugging Face

When to choose

• you need a pretrained generalist manipulation policy for robot arms
• you want to finetune a robot foundation model with limited compute
• you work with RGB camera inputs, language instructions, or goal-image conditioning
• you are doing robotics research on multi-task, multi-embodiment policies

When to avoid

• you need locomotion, navigation, or mobile robot policies rather than manipulation
• you require real-time control on low-power hardware without a GPU
• you want a plug-and-play production robotics stack rather than a research codebase
• you need non-Python or non-JAX integration

Facets

library · maturity active

machine-learning deep-learning llm-inference robotics machine-learning deep-learning artificial-intelligence python cross-platform robot-policy diffusion-policy transformer jax open-x-embodiment robot-manipulation finetuning generalist-robot-policy gpu linux

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markdown · JSON · MCP: product_card(name="octo-models/octo")