GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

By Nvidia, Johan Bjorck, Fernando Castañeda, Nikita Cherniadev, Xingye Da, Runyu Ding, LinxiJimFan, Yu Fang, Dieter Fox, Fengyuan Hu and 32 more. arXiv.org, 2025

1,085 citations recorded by the source corpus.

General-purpose robots need a versatile body and an intelligent mind. Recent advancements in humanoid robots have shown great promise as a hardware platform for building generalist autonomy in the human world. A robot foundation model, trained on massive and diverse data sources, is essential for enabling the robots to reason about novel situations, robustly handle real-world variability, and rapidly learn new tasks. To this end, we introduce GR00T N1, an open foundation model for humanoid robots. GR00T N1 is a Vision-Language-Action (VLA) model with a dual-system architecture. The vision-language module (System 2) interprets the environment through vision and language instructions. The subsequent diffusion transformer module (System 1) generates fluid motor actions in real time. Both modules are tightly coupled and jointly trained end-to-end. We train GR00T N1 with a heterogeneous mixture of real-robot trajectories, human videos, and synthetically generated datasets. We show that our generalist robot model GR00T N1 outperforms the state-of-the-art imitation learning baselines on standard simulation benchmarks across multiple robot embodiments. Furthermore, we deploy our model on the Fourier GR-1 humanoid robot for language-conditioned bimanual manipulation tasks, achieving strong performance with high data efficiency.

Subfields: Artificial Intelligence, Biomedical Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/731c50b0d6af4c1cb8d95f506541681ea487973b

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Citations in the corpus

V-JEPA 2: Self-Supervised Video Models Enable Understanding, Prediction and Planning (2025, neutral): “…have been pre-trained on internet scale video and text data, which are then fine-tuned (or adapted) to also predict actions by using behavior cloning from expert demonstrations (Driess et al., 2023; Brohan et al., 2023; Black et al., 2024; Kim et al., 2024; Bjorck et al., 2025; Black et al., 2025).”
π0.5: a Vision-Language-Action Model with Open-World Generalization (2025, positive): “Vision-language-action models (VLAs) [23, 92, 42, 8, 83, 90, 55, 45, 3, 75, 64, 76, 84, 7, 37] offer an appealing solution: by ﬁne-tuning pre-trained vision-language models for robot control, VLAs can leverage the semantic knowledge acquired from web-scale pretraining and bring it to bear on the…”
SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics (2025, neutral): “Additionally, the high heterogeneity of robot morphologies, sensors, actuation modes, control frequencies, and data formats results in "data islands" Bjorck et al. (2025)–scattered robotics datasets whose integration proves challenging.”