FriendliAI / MiMo-Embodied-7B

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.

II. Model Capabilities

III. Model Details

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

Spatial Understanding

Autonomous Driving Benchmarks

Single-View Image & Multi-View Video

Multi-View Image & Single-View Video

General Visual Understanding Benchmarks

Results marked with * are obtained using our evaluation framework.

V. Case Visualization

Embodied AI

Affordance Prediction

Task Planning

Spatial Understanding

Autonomous Driving

Environmental Perception

Status Prediction

Driving Planning

Real-world Tasks

Embodied Navigation

Embodied Manipulation

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}, 
}