| --- |
| license: cc-by-4.0 |
| pretty_name: PointMaze Medium (Temporal Straightening) |
| task_categories: |
| - robotics |
| - reinforcement-learning |
| tags: |
| - world-model |
| - planning |
| - point-maze |
| - maze2d |
| - d4rl |
| - temporal-straightening |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # PointMaze Medium |
|
|
| The PointMaze-Medium dataset from |
| [Temporal Straightening for Latent Planning](https://arxiv.org/abs/2603.12231) |
| (Wang et al., 2026). It has 4,000 trajectories of 100 steps in the D4RL |
| `maze2d-medium-v0` maze, rendered at 224×224. It extends the umaze set in |
| [DINO-WM](https://huggingface.co/datasets/gaoyuezhou/dino_wm) and uses the same |
| file layout. |
|
|
| ## Layout |
|
|
| ``` |
| point_maze_medium/ |
| ├── obses/episode_000.pth … episode_3999.pth # uint8 [100, 224, 224, 3] per episode |
| ├── states.pth # float64 [4000, 100, 4]: x, y, vx, vy |
| ├── actions.pth # float64 [4000, 100, 2]: in [-1, 1] |
| ├── seq_lengths.pth # int64 [4000], all 100 |
| ├── metadata.pth # generation settings |
| ├── examples/ # mp4 clips of the first ten episodes |
| └── MANIFEST.sha256 # checksums: sha256sum -c MANIFEST.sha256 |
| ``` |
|
|
| Frame `t` and `states[:, t]` are both taken after step `t`; `t = 0` is the |
| reset. There is no stored split. The paper splits by trajectory at load time, |
| 3,600 train and 400 validation. |
|
|
| ## Generation |
|
|
| The point-maze environment from the |
| [temporal-straightening code](https://github.com/agentic-learning-ai-lab/temporal-straightening), |
| which builds on DINO-WM (d4rl 1.1, mujoco-py 2.1.2.14, MuJoCo 2.1.0, gym 0.23.1). |
| Actions are uniform in [-1, 1]² with numpy seed 0. Episode `i` starts from |
| `env.sample_random_init_goal_states(100 * i)`. |
|
|
| ## Loading |
|
|
| ```python |
| import torch |
| from huggingface_hub import snapshot_download |
| |
| root = snapshot_download("agentic-learning-ai-lab/point_maze_medium", repo_type="dataset") |
| states = torch.load(f"{root}/states.pth") # [4000, 100, 4] |
| actions = torch.load(f"{root}/actions.pth") # [4000, 100, 2] |
| frames = torch.load(f"{root}/obses/episode_000.pth") # uint8 [100, 224, 224, 3] |
| ``` |
|
|
| To use it with the temporal-straightening code, place or symlink the downloaded |
| directory at `$DATASET_DIR/point_maze_medium`. |
|
|
| ## License |
|
|
| CC BY 4.0. Please cite the paper below if you use the data. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{wang2026temporal_straightening, |
| title={Temporal Straightening for Latent Planning}, |
| author={Wang, Ying and Bounou, Oumayma and Zhou, Gaoyue and Balestriero, Randall and Rudner, Tim GJ and LeCun, Yann and Ren, Mengye}, |
| journal={arXiv preprint arXiv:2603.12231}, |
| year={2026} |
| } |
| |
| @article{zhou2024dinowm, |
| title={DINO-WM: World Models on Pre-trained Visual Features enable Zero-shot Planning}, |
| author={Zhou, Gaoyue and Pan, Hengkai and LeCun, Yann and Pinto, Lerrel}, |
| journal={arXiv preprint arXiv:2411.04983}, |
| year={2024} |
| } |
| |
| @article{fu2020d4rl, |
| title={D4RL: Datasets for Deep Data-Driven Reinforcement Learning}, |
| author={Fu, Justin and Kumar, Aviral and Nachum, Ofir and Tucker, George and Levine, Sergey}, |
| journal={arXiv preprint arXiv:2004.07219}, |
| year={2020} |
| } |
| ``` |
|
|
