Add dataset card, link to paper and project resources

This PR adds a comprehensive dataset card for EBench, including metadata for the robotics task, the MIT license, relevant tags, and links to the paper, project page, documentation, and GitHub repository.

README.md

@@ -0,0 +1,35 @@
+---
+license: mit
+task_categories:
+- robotics
+tags:
+- lerobot
+- mobile-manipulation
+- vla
+---
+
+# EBench: Elemental Mobile Manipulation Benchmark Dataset
+
+[Project Page](https://internrobotics.github.io/EBench-home/) | [Paper](https://huggingface.co/papers/2606.18239) | [GitHub](https://github.com/InternRobotics/EBench) | [Documentation](https://internrobotics.github.io/EBench-doc/)
+
+EBench is an indoor VLA (Vision-Language-Action) manipulation benchmark built on NVIDIA Isaac Sim. Instead of compressing a model's behavior into a single overall success rate, it produces a **multi-axis capability profile** that exposes what a model is good at — and where it overfits.
+
+This repository contains the training trajectories (stored in the [LeRobot](https://github.com/huggingface/lerobot) format) used in the EBench evaluation suite.
+
+## Key Features
+- **Three manipulation regimes in one benchmark** — covers *long-horizon*, *dexterous & precise*, and *mobile* manipulation.
+- **5-axis atomic diagnostic** — every task is labelled by *Scene · Atomic Skill · Horizon · Precision · Mobility*.
+- **4-axis generalization tests** — controlled perturbations along *Object · Background · Instruction · Mixed*.
+- **Strict train / test isolation** — validation and unseen splits are open for tuning, while a held-out test split drives the leaderboard.
+
+## Citation
+
+```bibtex
+@misc{ebench2026,
+  title  = {EBench: Elemental Mobile Manipulation Benchmark},
+  author = {Shanghai AI Laboratory},
+  year   = {2026},
+  note   = {Preprint coming soon},
+  url    = {https://internrobotics.github.io/EBench-doc/}
+}
+```