| --- |
| license: apache-2.0 |
| task_categories: |
| - robotics |
| --- |
| |
| # RoboMME Training Data (Pickle Format) |
|
|
| [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 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 |
|
|
| ``` |
| . |
| ├── data # zipped pickle files |
| ├── features # zipped precompute siglip embeddings |
| ├── meta # statistics for robomme |
| ├── 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} |
| } |
| ``` |
