Runway Releases Open-Source Praxis-1: A Video-Trained Robot Control Model Claiming Top Performance in Its Category
Runway releases Praxis-1, an open-source robot control model trained on video data, claiming leading results on Nvidia's RoboLab-120 benchmark, though unverified independently.
AI company Runway released the open weights and source code for its robot control model, Praxis-1, on November 14, 2026, under the commercially permissive Apache 2.0 license. The model's core innovation lies in its "video-to-robot" transfer learning path: it first learns how objects move and interact from ordinary footage, then fine-tunes on videos of human actions and limited robot data. This allows it to predict environmental changes and calculate corresponding robot movements based on camera images, text instructions, and position data.
Runway calls Praxis-1 a "World Action Model," aiming to reduce reliance on expensive and time-consuming task-specific robot datasets. According to the company, adapting the model to a Franka robot arm required only about 277 hours of recordings from the public DROID dataset, while a dual-arm robot used between 1,400 and 2,600 hours of training material. Whether this approach actually needs less robot data overall than comparable models without general video pretraining remains an open question.
In terms of performance, Runway evaluated Praxis-1 itself on Nvidia's RoboLab-120 simulation benchmark, which tests sorting, grasping, or stacking across 120 tasks. The results show a 46.2 percent success rate, higher than FLUX 3 Action (42.9 percent), also built as a World Action Model, placing it at the top of that category. However, on the overall leaderboard, it ranks fifth behind two AI agents and two robot models with different architectures. It must be emphasized that all test results were run by Runway and have not been independently verified.
Beyond simulations, Runway conducted comparative experiments on a real Franka robot arm. When completing ten identical tasks (such as placing a banana on a plate or closing a drawer) with ten attempts each, Praxis-1 succeeded 88 times, while FLUX 3 Action managed 80. Additionally, in transfer tests on a mobile robot platform never seen before, after fine-tuning on about 95 hours of data, Praxis-1 hit an average success rate of 80 percent in simulated tests, significantly higher than Google's original RT-1 model's 52.4 percent.