Create dataset card with metadata and links to paper/code

README.md

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+---
+license: mit
+task_categories:
+- robotics
+tags:
+- lerobot
+- vla
+- mobile-manipulation
+---
+
+# EBench: Elemental Mobile Manipulation Benchmark
+
+This repository contains the training trajectories (stored in LeRobot format) for **EBench**, 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, EBench produces a multi-axis capability profile that exposes what a model is good at and where it overfits.
+
+- **Paper:** [EBench: Elemental Diagnosis of Generalist Mobile Manipulation Policies](https://huggingface.co/papers/2606.18239)
+- **Project Page:** [EBench Homepage](https://internrobotics.github.io/EBench-home/)
+- **Documentation:** [EBench Docs](https://internrobotics.github.io/EBench-doc/)
+- **GitHub Repository:** [InternRobotics/EBench](https://github.com/InternRobotics/EBench)
+
+## 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*.
+- **LeRobot Format** — trajectories are structured using Hugging Face's `lerobot` data format, making them ready for loading and training.
+
+## Citation
+
+If you find EBench useful in your research, please cite:
+
+```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/}
+}
+```