# Runway releases open-weight Praxis-1 robot controller

> The model turns camera images and instructions into robot movements. Runway says it led a simulation benchmark, but its tests have not been independently checked.

Oossa · 2026-11-14 · https://oossa.com/en/runway-releases-open-weight-praxis-1-robot-controller

Runway has released Praxis-1, a robot-control model whose weights and source code are available under the Apache 2.0 license, which permits commercial use. The model takes in camera images, text instructions and information about a robot’s position, then predicts how the scene will change and what movements the robot should make. It adjusts its control as new camera frames arrive.

Runway first introduced Praxis-1 in late September and initially shared it only with selected partners. Robotics companies Standard Bots, Noble Machines and Ultra have since tested it on different systems, including a humanoid robot, an industrial arm and a dual-arm warehouse robot. Developers can download it from Hugging Face after signing up and agreeing to access terms; its code is on GitHub.

## How does it perform?

Praxis-1 builds on a video model trained to understand how objects move and interact. Runway fine-tuned it with videos of human actions and robot data, aiming to make use of what the model learned from ordinary footage. The approach may reduce how much robot-specific data is needed, but that has not yet been established: Runway still used about 277 hours of DROID robot recordings to adapt Praxis-1 to a Franka arm, and 1,400 to 2,600 hours of material for a dual-arm robot.

In Nvidia’s RoboLab-120 simulation benchmark, which tests 120 object-sorting, grasping and stacking tasks, Praxis-1 scored a 46.2% success rate. FLUX 3 Action scored 42.9% under the same conditions. Praxis-1 ranked first among World Action Models, a category of systems that predict how a scene will change to guide actions, and fifth overall. Runway ran the tests; the results have not been independently verified.

On a real Franka arm, Praxis-1 completed 88 of 100 attempts across ten tasks, compared with 80 for FLUX 3 Action. In a separate simulation using a robot platform excluded from its earlier training, Praxis-1 reached 80% after fine-tuning on about 95 hours of robot data. Runway reports that Google’s original RT-1 scored 52.4% on the same benchmark.

## What are the limits?

The base model needs additional training to work with a new robot. Its standard version requires a GPU with 80 GB of memory. Runway estimates that a compressed version can fit on an RTX 5090 with 32 GB, though its peak memory use has not been measured. Runway also says Praxis-1 can confuse similar-looking objects and struggle with vague instructions. Complex tasks need to be broken into clear steps because the model cannot yet reliably plan long action sequences on its own.

## The facts

- Praxis-1’s weights and source code are released under the Apache 2.0 license.
- Runway reports a 46.2% success rate on Nvidia’s RoboLab-120 benchmark, compared with 42.9% for FLUX 3 Action.
- On a real Franka robot arm, Praxis-1 succeeded in 88 of 100 attempts across ten tasks; FLUX 3 Action succeeded in 80.
- The standard version requires a GPU with 80 GB of memory; Runway estimates the compressed version can fit on an RTX 5090 with 32 GB.

## Why it matters

Robot developers can download Praxis-1 and inspect or adapt its code under a license that permits commercial use, though the base model needs extra training for each new robot. Runway’s results are promising, but they are company-run and not independently verified, so it remains unclear how well the model will perform in other developers’ settings or whether video pretraining actually reduces data needs.

## Sources & references

1. [Runway's open-source Praxis-1 turns video AI into a robot controller](https://the-decoder.com/runways-open-source-praxis-1-turns-video-ai-into-a-robot-controller/) – The Decoder, 2026-11-14

Last updated: 2026-11-14