metadata
license: apache-2.0
task_categories:
- robotics
RoboMME Training Data (Pickle Format)
Paper | Website | Benchmark Code | Policy Learning Code
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 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:
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
@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}
}