| 🤗 HuggingFace | 📔 Technical Report |
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},
}