update readme

README.md

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----
-license: mit
----
+---
+license: mit
+tags:
+- Tactile Sensing
+- Vibro-Acoustic Sensing
+- Contact Localization
+- Deep Learning
+pretty_name: Vibrosense Dataset
+size_categories:
+- n>1T
+---
+# Vibro-Sense: Robust Vibration-based Impulse Response Localization and Trajectory Tracking for Robotic Hands
+### Paper page can be found [here](https://wzaielamri.github.io/publication/vibrosense).
+### Github Repository can be found [here](https://github.com/wzaielamri/vibrosense).
+
+
+# Vibrosense.
+
+
+
+This repository provides the full dataset for the Vibro-Sense project, enabling robust, affordable, and scalable contact perception for robotic hands using vibro-acoustic sensing. Our approach leverages seven low-cost piezoelectric microphones and an Audio Spectrogram Transformer to decode vibrational signatures generated during physical interaction. The system achieves high-accuracy whole-body touch localization, robust trajectory tracking, and material-aware perception, and is resilient to robot motion.
+
+**Note:** The dataset is generated and stored using the [webdataset](https://github.com/webdataset/webdataset) format for efficient large-scale data handling and streaming.
+
+## Dataset Structure
+
+The dataset is organized into several main directories:
+
+- `localisation/`: Contains data for touch localization and material detection tasks, including filtered and processed signals for different materials (soft-plastic (cap), metal, plastic, wood).
+- `quickdraw/`: Contains data for trajectory following task, with both fixed and moving hand conditions, and for different materials.
+- `quickdraw_testset/`: Test set for trajectory following, organized similarly to `quickdraw/`.
+- `objects/`: Contains object classification data collected with three microphones.
+
+## Tasks
+
+This dataset supports four main tasks:
+
+1. **Touch Localization**
+  - Predict the contact location on the robotic hand using vibrational signals.
+  - Data: `localisation/filtered/` and `localisation/processed_*` directories.
+
+2. **Trajectory Following (Quickdraw)**
+  - Track and reconstruct the trajectory of contact points during dynamic interactions.
+  - Data: `quickdraw/` and `quickdraw_testset/` directories.
+
+3. **Object Classification**
+  - Classify the object being touched based on the vibrational response.
+  - Data: `objects/dataset/`.
+
+4. **Material Detection (using Localisation Dataset)**
+  - Identify the material of the contact surface (e.g., metal, plastic, wood, soft-plastic (cap)) using the localisation dataset.
+  - Data: `localisation/filtered/` and `localisation/processed_*` directories.
+
+## Getting Started
+
+All datasets are provided in processed form, ready for training and evaluation. Please refer to the [paper](https://wzaielamri.github.io/publication/vibrosense) and [code repository](https://github.com/wzaielamri/vibrosense) for details on model training and evaluation.
+
+## Citation
+
+```bibtex
+@InProceedings{ZaiElAmri2026VibroSense,
+  author = {Zai El Amri, Wadhah and {Navarro-Guerrero}, Nicol{\'a}s},
+  title = {"Vibro-Sense: Robust Vibration-based Impulse Response Localization and Trajectory Tracking for Robotic Hands"},
+  booktitle = {"ArXiv Preprint arXiv:2601.20555, Submitted to Autonomous Robots Springer Journal"},
+  year={2026},
+}
+```