MiMo-Embodied-7B

<div align="center"> <img src="./assets/xfmlogo.svg" width=600> </div> <br/> <div align="center" style="line-height: 1;"> | <a href="https://huggingface.co/XiaomiMiMo/MiMo-Embodied-7B" target="_blank">🤗 HuggingFace</a> &nbsp;| <a href="https://arxiv.org/abs/2511.16518" target="_blank">📔 Technical Report</a> &nbsp;| <br/> </div>

I. Introduction

MiMo-Embodied, a powerful cross-embodied vision-language model that shows state-of-the-art performance in both autonomous driving and embodied AI tasks, the first open-source VLM that integrates these two critical areas, significantly enhancing understanding and reasoning in dynamic physical environments.

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II. Model Capabilities

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III. Model Details

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IV. Evaluation Results

MiMo-Embodied demonstrates superior performance across 17 benchmarks in three key embodied AI capabilities: Task Planning, Affordance Prediction, and Spatial Understanding, significantly surpassing existing open-source embodied VLM models and rivaling closed-source models.

Additionally, MiMo-Embodied excels in 12 autonomous driving benchmarks across three key capabilities: Environmental Perception, Status Prediction, and Driving Planning—significantly outperforming both existing open-source and closed-source VLM models, as well as proprietary VLM models.

Moreover, evaluation on 8 general visual understanding benchmarks confirms that MiMo-Embodied retains and even strengthens its general capabilities, showing that domain-specialized training enhances rather than diminishes overall model proficiency.

Embodied AI Benchmarks

Affordance & Planning

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Spatial Understanding

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Autonomous Driving Benchmarks

Single-View Image & Multi-View Video

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Multi-View Image & Single-View Video

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General Visual Understanding Benchmarks

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Results marked with * are obtained using our evaluation framework.

V. Case Visualization

Embodied AI

Affordance Prediction

<div align="center"> <img src="./assets/afford-1.svg" width=800> </div>

Task Planning

<div align="center"> <img src="./assets/planning-1.svg" width=800> </div>

Spatial Understanding

<div align="center"> <img src="./assets/spatial-1.svg" width=800> </div>

Autonomous Driving

Environmental Perception

<div align="center"> <img src="./assets/ad-perception-1.svg" width=800> </div>

Status Prediction

<div align="center"> <img src="./assets/ad-prediction-1.png" width=800> </div>

Driving Planning

<div align="center"> <img src="./assets/ad-planning-1.png" width=800> </div>

Real-world Tasks

Embodied Navigation

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Embodied Manipulation

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VI. Citation

@misc{hao2025mimoembodiedxembodiedfoundationmodel,
      title={MiMo-Embodied: X-Embodied Foundation Model Technical Report}, 
      author={Xiaomi Embodied Intelligence Team},
      year={2025},
      eprint={2511.16518},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2511.16518}, 
}