Upload README.md

README.md

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+
+<!-- ```yaml
+datasets:
+- MINT-SJTU/RoboFAC-dataset
+base_model:
+- Qwen/Qwen2.5-VL-7B-Instruct
+``` -->
+# Model Card for RoboFAC-7B
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)]() [![Paper](https://img.shields.io/badge/Paper-PDF-red)](https://arxiv.org/abs/2505.12224) [![Dataset](https://img.shields.io/badge/Dataset-Huggingface-green)](https://huggingface.co/datasets/MINT-SJTU/RoboFAC-dataset) [![Model](https://img.shields.io/badge/Model-Huggingface-yellow)](https://huggingface.co/MINT-SJTU/RoboFAC-7B)
+RoboFAC-7B is a large-scale vision-language model specifically finetuned for **robotic failure understanding and correction**. It takes in visual observations of robot executions (usually video frames) and outputs detailed answers to questions that analyze, diagnose, and propose corrections for robotic manipulation failures.
+
+## Model Details
+
+### Model Description
+
+* **Developed by:** [MINT Lab, Shanghai Jiao Tong University](https://mint-sjtu.github.io/)
+* **Model type:** Vision-Language Model (VLM) for robotic failure analysis
+* **Languages:** English (instruction-tuned for robotic QA)
+* **License:** Apache 2.0
+* **Finetuned from model:** Qwen/Qwen2.5-VL-7B-Instruct
+
+
+---
+
+## Uses
+
+### Direct Use
+
+The model is intended to be used in robotic systems as an *external critic*, to:
+
+* Perform **task understanding** by answering what the robot is doing.
+* Conduct **failure diagnosis** by identifying where and why it failed.
+* Generate **correction suggestions** based on visual observations.
+
+### Downstream Use
+
+The model can be integrated into:
+
+* Vision-language control pipelines (e.g., VLA systems)
+* Robotic operation monitoring tools
+* Training agents with self-improvement capabilities
+---
+
+## Quickstart
+
+```python
+from transformers import AutoProcessor, AutoModelForVision2Seq
+
+model = AutoModelForVision2Seq.from_pretrained("MINT-SJTU/RoboFAC-7B")
+processor = AutoProcessor.from_pretrained("MINT-SJTU/RoboFAC-7B")
+
+# Example usage with image frames and a question
+inputs = processor(images=[...], text="Why did the robot fail?", return_tensors="pt").to("cuda")
+outputs = model.generate(**inputs)
+print(processor.batch_decode(outputs, skip_special_tokens=True))
+```
+
+
+## Citation
+
+**BibTeX:**
+
+```bibtex
+@misc{lu2025robofaccomprehensiveframeworkrobotic,
+  title={RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction},
+  author={Weifeng Lu and Minghao Ye and Zewei Ye and Ruihan Tao and Shuo Yang and Bo Zhao},
+  year={2025},
+  eprint={2505.12224},
+  archivePrefix={arXiv},
+  primaryClass={cs.RO},
+  url={https://arxiv.org/abs/2505.12224}
+}
+```
+