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GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

Authors: Johan Bjorck, Fernando Castaneda, Nikita Cherniadev, Xingye Da, Runyu Ding, Linxi "Jim" Fan, Yu Fang, Dieter Fox, Fengyuan Hu, Spencer Huang, Joel Jang, Zhenyu Jiang, Jan Kautz, Kaushil Kundalia, Lawrence Lao, Zhiqi Li, Zongyu Lin, Kevin Lin, Guilin Liu, Edith Llontop, Loic Magne, Ajay Mandlekar, Avnish Narayan, Soroush Nasiriany, Scott Reed, You Liang Tan, Guanzhi Wang, Zu Wang, Jing Wang, Qi Wang, Jiannan Xiang, Yuqi Xie, Yinzhen Xu, Zhenjia Xu, Seonghyeon Ye, Zhiding Yu, Ao Zhang, Hao Zhang, Yizhou Zhao, Ruijie Zheng, Yuke Zhu

Published: 2025 (Preprint)

Source: arXiv

Algorithm: GR00T N1

arXiv: 2503.14734

Summary

GR00T N1 frames humanoid control as a robot foundation-model problem, pairing a vision-language reasoning module with a diffusion-transformer action generator that produces continuous motor commands. Its main contribution is an open VLA model trained from a heterogeneous mix of robot demonstrations, human videos, and synthetic data, showing cross-embodiment simulation gains and real humanoid bimanual manipulation with relatively high data efficiency.

Abstract

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.

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Paper arxiv.org

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arXiv Abstract 2503.14734 arXiv PDF 2503.14734 arXiv HTML 2503.14734

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Tags

  • Robotics

  • Humanoid robots

  • Robot foundation models

  • Vision-language-action models

  • Diffusion policies

  • Imitation learning

  • Bimanual manipulation

  • Embodied AI

  • Synthetic data