Add dataset card, paper link, and robotics metadata

Hi, I'm Niels from the community science team at Hugging Face. This PR adds a dataset card for the Tabero benchmark, including a link to the paper and GitHub repository, as well as metadata for the robotics task. It also includes sample usage instructions for downloading the data as found in the official repository.

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}
+}
+```