Improve model card and add metadata

Hi! I'm Niels from the community science team at Hugging Face. I've updated your model card to include:
- Metadata for the `pipeline_tag`, `library_name`, and `base_model`.
- Relevant tags for better discoverability.
- Links to the paper, project page, and official GitHub repository.
- A summary of the model architecture.
- A "Getting Started" section with evaluation instructions from your README.
- The BibTeX citation for your ICRA 2026 paper.

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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
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-## Training Details
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-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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 ---
 library_name: transformers
+pipeline_tag: image-text-to-text
+base_model: google/paligemma2-3b-mix-224
+tags:
+- robotics
+- failure-detection
+- vision-language
 ---
+# I-FailSense: Towards General Robotic Failure Detection with Vision-Language Models
+I-FailSense is a vision-language model (VLM) framework designed to detect language-conditioned robotic failures. It focuses on identifying **semantic misalignment errors**, where a robot executes a task that is semantically meaningful but inconsistent with the user's instruction.
+- **Paper:** [I-FailSense: Towards General Robotic Failure Detection with Vision-Language Models](https://huggingface.co/papers/2509.16072)
+- **Project Page:** [https://clemgris.github.io/I-FailSense/](https://clemgris.github.io/I-FailSense/)
+- **Repository:** [https://github.com/clemgris/I-FailSense](https://github.com/clemgris/I-FailSense)
+## Model Description
+The model architecture consists of a base VLM (PaliGemma-2 3B) fine-tuned using LoRA, combined with lightweight classification heads (FS blocks) attached to internal layers. An ensembling mechanism aggregates predictions from these blocks to provide grounded arbitration for failure detection. While trained primarily on semantic misalignment, I-FailSense generalizes well to broader robotic failure categories and different environments.
+## How to Get Started
+To use or evaluate the model, please use the implementation provided in the [official GitHub repository](https://github.com/clemgris/I-FailSense).
+### Evaluation
+You can evaluate the model (using both the LoRA weights and the FS block weights) on the Calvin dataset with the following command:
+```bash
+python src/evaluate.py \
+    --vlm_model_id ACIDE/FailSense-Calvin-1p-3b \
+    --fs_id FS/FailSense-Calvin-1p-3b \
+    --dataset_name calvin \
+    --result_folder results_calvin_1p
+```
+## Citation
+If you find this work useful, please cite:
+```bibtex
+@inproceedings{ifailsense2026,
+  title        = {I-FailSense: Towards General Robotic Failure Detection with Vision-Language Models},
+  author       = {Clemence Grislain and Hamed Rahimi and Olivier Sigaud and Mohamed Chetouani},
+  booktitle    = {Proceedings of the International Conference on Robotics and Automation (ICRA)},
+  year         = {2026},
+  url          = {https://arxiv.org/abs/2509.16072}
+}
+```