| --- |
| license: mit |
| pretty_name: Memory-T-Bench |
| tags: |
| - robotics |
| - imitation-learning |
| - diffusion-policy |
| - memory |
| - pomdp |
| - push-t |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - robotics |
| --- |
| |
| # Memory-T-Bench |
|
|
| Partially observable, contact-rich 2D pushing tasks derived from |
| [Push-T](https://diffusion-policy.cs.columbia.edu/), introduced in |
| **"Remember what you did?: Learning Behavioral Memories for Partially Observable |
| Object Manipulation"** ([paper](https://arxiv.org/abs/2606.21188) · [project page](https://robo-camp.github.io/)). The |
| current observation never reveals what to do next; the policy must remember |
| what it already did or found out. |
|
|
| The release ships **only the modalities a real-world policy has access to**: |
| image, robot state and action. No privileged hidden-state arrays are included, |
| so a model cannot read the variable it is supposed to remember. |
|
|
| ## Tasks |
|
|
| <table align="center"> |
| <tr> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-T-Bench/resolve/main/multi-goals/preview.gif" width="200"><br><b>Multi-Goals</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-T-Bench/resolve/main/swap-direct/preview.gif" width="200"><br><b>Swap-Direct</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-T-Bench/resolve/main/swap-shuffle/preview.gif" width="200"><br><b>Swap-Shuffle</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-T-Bench/resolve/main/find-track/preview.gif" width="200"><br><b>Find-Track</b></td> |
| </tr> |
| </table> |
|
|
| | folder | task | what must be remembered | success criterion | episodes | steps | |
| |---|---|---|---|---:|---:| |
| | [`multi-goals`](multi-goals/) | **Multi-Goals** — Push the gray T block onto each of the three target areas, each of them only once, in any order. | which targets are already done | each goal reached at ≥ 85 % block–goal overlap, none twice | 320 | 94,636 | |
| | [`swap-direct`](swap-direct/) | **Swap-Direct** — Push the blue and red T blocks to the middle, stop on the black dot, then move each block into the other's starting area. | where each T block started | both blocks ≥ 85 % overlap with the other's start area | 320 | 105,615 | |
| | [`swap-shuffle`](swap-shuffle/) | **Swap-Shuffle** — Use the empty target area as a buffer to shuffle the blue and red T blocks into each other's starting area. | where each T block started | both blocks ≥ 85 % overlap with the other's start area | 320 | 118,629 | |
| | [`find-track`](find-track/) | **Find-Track** — Push the gray T block from the starting area through one of three tracks to the target area on the opposite side; two tracks are impassable. | which track is passable | ≥ 85 % overlap with the target area, no re-tried track | 320 | 91,807 | |
|
|
| Each folder holds `data.zarr.zip` and a `preview.gif`. Environments, data |
| collection scripts and the CAMP training code are in the |
| [CAMP repository](https://robo-camp.github.io/) (`memory_t_bench` package). |
|
|
| ## Quick start |
|
|
| ```python |
| from huggingface_hub import hf_hub_download |
| import zarr |
| |
| path = hf_hub_download(repo_id="harrywang01/Memory-T-Bench", |
| filename="multi-goals/data.zarr.zip", repo_type="dataset") |
| root = zarr.open(zarr.ZipStore(path, mode="r"), mode="r") |
| print(root.tree()) |
| ends = root["meta/episode_ends"][:] # exclusive end index of every episode |
| ep0 = slice(0, int(ends[0])) |
| img, state, action = root["data/img"][ep0], root["data/state"][ep0], root["data/action"][ep0] |
| ``` |
|
|
| ## Data format |
|
|
| Each task is a single `data.zarr.zip` in the Diffusion Policy replay-buffer |
| convention: |
|
|
| ``` |
| data.zarr.zip |
| ├── data/ |
| │ ├── action (N, 2) float32 target agent position (pixels) |
| │ ├── img (N, 96, 96, 3) uint8 rendered observation |
| │ ├── state (N, 2) float32 agent position (pixels) |
| │ └── language_instruction (N,) <U200 constant per task |
| └── meta/ |
| └── episode_ends (E,) int64 exclusive end indices |
| ``` |
|
|
| Arrays are flat over all episodes; episode `i` spans |
| `[episode_ends[i-1], episode_ends[i])`. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @misc{wang2026rememberdidlearningbehavioral, |
| title={Remember what you did?: Learning Behavioral Memories for Partially Observable Object Manipulation}, |
| author={Kuancheng Wang and Seungho Yeom and Jinglin Cao and Yuheng Zhi and Nikhil Shinde and Michael Yip}, |
| year={2026}, |
| eprint={2606.21188}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.RO}, |
| url={https://arxiv.org/abs/2606.21188}, |
| } |
| ``` |
|
|
| ## License |
|
|
| MIT — see [`LICENSE`](LICENSE). |
|
|
