MiMo-Embodied: X-Embodied Foundation Model Technical Report

arXiv cs.RO / 4/29/2026

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Key Points

  • The paper releases MiMo-Embodied, an open-source foundation model designed to perform well across both autonomous driving and embodied AI tasks.
  • The model achieves state-of-the-art results, setting new records on 17 embodied AI benchmarks spanning task planning, affordance prediction, and spatial understanding.
  • It also excels on 12 autonomous driving benchmarks covering environmental perception, status prediction, and driving planning.
  • The authors report that multi-stage learning, curated data construction, and CoT/RL fine-tuning enable strong positive transfer between the two domains.
  • Detailed design and training analysis are provided to support further research, with code and models published on GitHub.

Abstract

We open-source MiMo-Embodied, the first cross-embodied foundation model to successfully integrate and achieve state-of-the-art performance in both Autonomous Driving and Embodied AI. MiMo-Embodied sets new records across 17 embodied AI benchmarks in Task Planning, Affordance Prediction and Spatial Understanding, while also excelling in 12 autonomous driving benchmarks across Environmental Perception, Status Prediction, and Driving Planning. Across these tasks, MiMo-Embodied significantly outperforms existing open-source, closed-source, and specialized baselines. Our results indicate that through multi-stage learning, curated data construction, and CoT/RL fine-tuning, these two domains exhibit strong positive transfer and mutually reinforce one another. We provide a detailed analysis of our model design and training methodologies to facilitate further research. Code and models are available at https://github.com/XiaomiMiMo/MiMo-Embodied.