GR00T N1: An Open Foundation Model for Generalist Humanoid Robots (2503.14734v2)

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.

Analysis of GR00T N1: An Open Foundation Model for Generalist Humanoid Robots

The development of autonomous robots capable of performing a wide range of tasks in human environments is a key aspiration in robotics research. The paper "GR00T N1: An Open Foundation Model for Generalist Humanoid Robots" introduces a significant contribution to this field by presenting the GR00T N1 model. This Vision-Language-Action (VLA) model represents a sophisticated approach to robotic autonomy, integrating advances in artificial intelligence, robotics, and large-scale data processing. Below, we offer a detailed examination of the design, training strategies, and evaluated capabilities of this model, emphasizing the technical innovations and outcomes revealed in the research.

Model Architecture and Design

GR00T N1 is structured with a dual-system architecture, comprising a Vision-Language Module (System 2) and a Diffusion Transformer-based action module (System 1). The Vision-Language Module utilizes the Eagle-2 VLM, processing incoming visual data and text-based instructions to construct a high-level understanding of the task environment. Subsequent actions are generated by the Diffusion Transformer, which leverages a cross-attention mechanism to amalgamate system outputs and produce real-time motor actions. This architectural design is notable for its modularity, allowing the system to integrate extensive perception and reasoning capabilities with effective action generation.

Training Across Heterogeneous Data Sources

A pivotal aspect of GR00T N1's development is its heterogeneous data corpus, structured as a "data pyramid." This pyramid composes extensive web data and human videos at its base, simulation-generated data at the center, and tangible real-world robot data at the peak. Such stratification aims to embed broad priors and physical grounding into the model. The benefits of this training regime are showcased in the paper, presenting enhanced generalization and robustness over task variants and environments.

The model training leverages specific innovations, including latent action inference and neural trajectories from video generative models, to circumvent the constraints of data scarcity. Latent action models are trained to predict target actions from video frames, providing robust supervision through pseudo-labeling. The adoption of advanced video generation techniques further enriches training data diversity, addressing limitations encountered in physical data collection.

Empirical Evaluation and Results

GR00T N1's performance was assessed through numerous simulated and real-world benchmarks, featuring tasks such as language-conditioned bimanual manipulation. In simulation environments, GR00T N1 consistently surpassed state-of-the-art imitation learning baselines, achieving significant success rates across various robot embodiments. This performance was matched by real-world experiments with the Fourier GR-1 humanoid robot, demonstrating strong task execution and data efficiency, particularly in low-data scenarios.

Crucially, the research presents systematic evaluations with varying dataset sizes, affirming the model's adaptability and generalist capabilities. The deployment of the GR00T N1 model on multiple embodiments, including tabletop arms and humanoid configurations, indicates its extensive applicability and flexibility.

Implications and Future Directions

The implications of this research extend across both theoretical and practical domains in AI and robotics. The GR00T N1 model not only provides a robust foundation for robotic autonomy but also advances methodologies in training generalist models, emphasizing the importance of diverse datasets and integrative learning architectures. This paper further ignites potential research avenues, including the exploration of more comprehensive domain representation in training data, ongoing enhancement of VLA model efficiencies, and extending capabilities into long-horizon loco-manipulation tasks.

Future developments could refine synthetic data generation models and extend GR00T N1's architecture to accommodate broader categories of humanoid tasks, moving closer to the eventual goal of ubiquitous, autonomous robots operating seamlessly in human environments. Overall, this paper contributes a well-rounded foundation equipped with promising methodologies and results, setting a high standard for forthcoming research efforts in robotic autonomy.