Improve dataset card: add robotics metadata, links, and sample usage

README.md

@@ -1,12 +1,22 @@
 ---
 license: apache-2.0
+task_categories:
+- robotics
 ---
 
 # RoboMME Training Data (Pickle Format)
 
-[Arxiv Paper](https://arxiv.org/abs/2603.04639) |  [HF Paper](https://huggingface.co/papers/2603.04639) |  [Website](https://robomme.github.io/) | [Benchmark Code](https://github.com/RoboMME/robomme_benchmark) | [Policy Learning Code](https://github.com/RoboMME/robomme_policy_learning)
+[Paper](https://huggingface.co/papers/2603.04639) | [Website](https://robomme.github.io/) | [Benchmark Code](https://github.com/RoboMME/robomme_benchmark) | [Policy Learning Code](https://github.com/RoboMME/robomme_policy_learning)
 
-This repo contains preprocessed pickle files for RoboMME training data and npy files for cached image tokens. We use this dataset in our [MME-VLA](https://github.com/RoboMME/robomme_policy_learning) experiments.
+This repository contains preprocessed pickle files for RoboMME training data and npy files for cached image tokens. This dataset is used in the [MME-VLA](https://github.com/RoboMME/robomme_policy_learning) experiments.
+
+RoboMME is a large-scale standardized benchmark for evaluating and advancing Vision-Language-Action (VLA) models in long-horizon, history-dependent scenarios. It comprises 16 manipulation tasks across four cognitively motivated suites:
+- **Counting** (Temporal memory)
+- **Permanence** (Spatial memory)
+- **Reference** (Object memory)
+- **Imitation** (Procedural memory)
+
+## Repository Structure
 
 ```
 .
@@ -16,4 +26,35 @@ This repo contains preprocessed pickle files for RoboMME training data and npy f
 ├── memer       # VLM subgoal training data for MemER (only used for symbolic memory)
 ├── qwenvl      # VLM subgoal training data for QwenVL  (only used for symbolic memory)
 └── README.md
+```
+
+## Sample Usage
+
+To evaluate on the test set using the `BenchmarkEnvBuilder` from the benchmark repository:
+
+```python
+task_id = "PickXtimes"
+episode_idx = 0
+env_builder = BenchmarkEnvBuilder(
+    env_id=task_id,
+    dataset="test",
+)
+
+env = env_builder.make_env_for_episode(episode_idx)
+obs, info = env.reset() # initial step
+task_goal = info['task_goal'][0]
+
+# Policy interaction loop
+# obs, _, terminated, truncated, info = env.step(action)
+```
+
+## Citation
+
+```bibtex
+@article{dai2026robomme,
+  title={RoboMME: Benchmarking and Understanding Memory for Robotic Generalist Policies},
+  author={Dai, Yinpei and Fu, Hongze and Lee, Jayjun and Liu, Yuejiang and Zhang, Haoran and Yang, Jianing and Chelsea Finn and Nima Fazeli and Joyce Chai},
+  journal={arXiv preprint arXiv:2603.04639},
+  year={2026}
+}
 ```