Add dataset card, paper link, and robotics metadata

README.md

@@ -0,0 +1,60 @@
+---
+task_categories:
+- robotics
+tags:
+- tactile-sensing
+- vision-language-action
+- vla
+- force-feedback
+---
+
+# Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language
+
+[Paper](https://huggingface.co/papers/2605.27886) | [GitHub](https://github.com/NathanWu7/Tabero)
+
+Tabero is a benchmark and model suite for gentle, language-conditioned robotic manipulation that demands fine-grained contact force perception. It addresses the scarcity of tactile data by presenting a data-efficient pipeline that repurposes open-source robot manipulation trajectories to generate diverse vision-tactile-language tasks. 
+
+The benchmark establishes a multidimensional evaluation protocol that measures task success alongside physical interaction quality, specifically targeting scenarios that require closed-loop force feedback.
+
+## Dataset Summary
+- **Tasks**: Gentle manipulation tasks (e.g., connector insertion, assembly) requiring contact force perception.
+- **Modality**: Vision (RGB, wrist camera), Touch (GelSight-based tactile or ContactForce), and Language.
+- **Format**: Data is available in HDF5/video formats, with support for LeRobot and OpenPI formats.
+
+## Sample Usage
+
+### Download Dataset
+You can download the associated data using the Hugging Face CLI:
+
+```bash
+# Download the LIBERO data
+huggingface-cli download NathanWu7/Isaaclab_Libero \
+  --repo-type dataset \
+  --local-dir /path/to/Isaaclab_Libero
+```
+
+### Tactile Calibration Assets
+When using tactile environments, you can download the calibration assets:
+
+```bash
+huggingface-cli download china-sae-robotics/Tactile_Manipulation_Dataset \
+  --repo-type dataset \
+  --local-dir /path/to/Tactile_manipulation_dataset
+```
+
+## Environment IDs
+The following environment IDs are associated with the benchmark:
+- `Isaac-Libero-Franka-Replay-Camera-ContactForce-v0`: replay with contact-force observations.
+- `Isaac-Libero-Franka-Hybrid-ContactForce-v0`: hybrid force-position control with contact force.
+- `Isaac-Libero-Franka-Replay-Camera-Tactile-v0`: replay with GelSight tactile sensors.
+- `Isaac-Libero-Franka-Hybrid-Tactile-v0`: hybrid tactile environment.
+
+## Citation
+```bibtex
+@article{tabero2024,
+  title={Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language},
+  author={Wu, Nathan and others},
+  journal={arXiv preprint arXiv:2605.27886},
+  year={2024}
+}
+```