# EO-1: An Open Unified Embodied Foundation Model for General Robot Control

## TL;DR

EO-1 is a unified embodied foundation model trained on a large multimodal dataset, enabling advanced robot control and reasoning across diverse tasks and embodiments, pushing towards human-level flexibility in open-world interactions.

## Contribution

The paper introduces EO-1, a novel unified architecture and a large-scale dataset, EO-Data1.5M, for multimodal embodied reasoning and robot control, achieving superior generalization and flexibility.

## Key findings

- EO-1 outperforms previous models in multimodal reasoning tasks.
- The model demonstrates strong generalization across multiple robot embodiments.
- Interleaved vision-text-action training improves open-world understanding.

## Abstract

The human ability to seamlessly perform multimodal reasoning and physical interaction in the open world is a core goal for general purpose embodied intelligent systems. Recent vision-language-action (VLA) models, which are co-trained on large-scale robot and visual-text data, have demonstrated notable progress in general robot control. However, they still fail to achieve human-level flexibility in interleaved reasoning and interaction. In this work, we introduce EO-Robotics, consists of EO-1 model and EO-Data1.5M dataset. EO-1 is a unified embodied foundation model that achieves superior performance in multimodal embodied reasoning and robot control through interleaved vision-text-action pre-training. The development of EO-1 is based on two key pillars: (i) a unified architecture that processes multimodal inputs indiscriminately (image, text, video, and action), and (ii) a massive, high-quality multimodal embodied reasoning dataset, EO-Data1.5M, which contains over 1.5 million samples with emphasis on interleaved vision-text-action comprehension. EO-1 is trained through synergies between auto-regressive decoding and flow matching denoising on EO-Data1.5M, enabling seamless robot action generation and multimodal embodied reasoning. Extensive experiments demonstrate the effectiveness of interleaved vision-text-action learning for open-world understanding and generalization, validated through a variety of long-horizon, dexterous manipulation tasks across multiple embodiments. This paper details the architecture of EO-1, the data construction strategy of EO-Data1.5M, and the training methodology, offering valuable insights for developing advanced embodied foundation models. Project Page: https://eo-robotics.ai/eo-1.