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README.md

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+---
+library_name: transformers
+pipeline_tag: image-text-to-text
+---
+
+# EMBGuard
+
+EMBGuard is the first MLLM-based safety guardrail for embodied agents designed to decouple physical risk reasoning from agent policy. By evaluating a (visual observation, action) pair, EMBGuard identifies hazardous configurations and provides natural language explanations of potential risks.
+
+- **Paper:** [EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents](https://huggingface.co/papers/2605.30924)
+- **Repository:** [https://github.com/dongwxxkchoi/EMBGuard](https://github.com/dongwxxkchoi/EMBGuard)
+
+## Model Description
+
+MLLM-powered embodied agents deployed in real-world environments encounter physical hazards. EMBGuard addresses the lack of explicit mechanisms for identifying these hazards by reasoning about action-conditioned risks. Despite its compact size (available in 2B and 4B variants), EMBGuard achieves performance competitive with proprietary MLLMs while significantly reducing false-positive rates that can hinder real-time deployment.
+
+The model is based on the Qwen3-VL architecture and has been fine-tuned to identify hazardous configurations and provide natural language explanations of potential risks.
+
+## Datasets
+
+The model was developed using the following datasets:
+- **EMBHazard:** A training dataset of 15.1K action-conditioned pairs.
+- **EMBGuardTest:** A benchmark of 329 manually curated real-world scenarios spanning seven physical risk categories.
+
+## Citation
+
+If you use EMBGuard in your research, please cite the following paper:
+
+```bibtex
+@article{choi2025embguard,
+  title={EMBGuard: Constructing Hazard-Aware Guardrails for Safe Planning in Embodied Agents},
+  author={Choi, Dongwook and others},
+  journal={arXiv preprint arXiv:2605.30924},
+  year={2025}
+}
+```