| --- |
| license: mit |
| pretty_name: Memory-Manip-Bench |
| tags: |
| - robotics |
| - imitation-learning |
| - diffusion-policy |
| - memory |
| - pomdp |
| - robosuite |
| - mujoco |
| size_categories: |
| - 100K<n<1M |
| task_categories: |
| - robotics |
| --- |
| |
| # Memory-Manip-Bench |
|
|
| Memory-dependent tabletop manipulation tasks for a Franka Panda (robosuite 1.5 / |
| MuJoCo 3), 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/)). Every task hides one piece of |
| information that the policy can only discover by interacting and must then |
| remember; forgetting it makes the episode fail. |
|
|
| ## Tasks |
|
|
| <table align="center"> |
| <tr> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/uncover_blocks.gif" width="200"><br><b>Uncover-Blocks</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/swap_block.gif" width="200"><br><b>Swap-Block</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/probe_insert.gif" width="200"><br><b>Probe-Insert</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/button_lightbulb.gif" width="200"><br><b>Button-Lightbulb</b></td> |
| </tr> |
| <tr> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/stack_lego.gif" width="200"><br><b>Stack-Lego</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/find_soda.gif" width="200"><br><b>Find-Soda</b></td> |
| <td align="center"><img src="https://huggingface.co/datasets/harrywang01/Memory-Manip-Bench/resolve/main/media/open_door.gif" width="200"><br><b>Open-Door</b></td> |
| </tr> |
| </table> |
|
|
| | file | task | env (`env_name`) | task camera | what must be remembered | |
| |---|---|---|---|---| |
| | `image_uncoverblock.hdf5` | **Uncover-Blocks** | `UncoverBlockVisionHard` | `agentview` | which covers were already lifted | |
| | `image_blockswap.hdf5` | **Swap-Block** | `FruitSwapEasy` | `peg_focus_view` | where each cube started | |
| | `image_insertpeg.hdf5` | **Probe-Insert** | `InsertPegVision` | `peg_focus_view` | which holes were already tried | |
| | `image_buttonlightbulb.hdf5` | **Button-Lightbulb** | `ButtonLightbulbVision` | `agentview` | which button toggles which bulb | |
| | `image_legostacking.hdf5` | **Stack-Lego** | `LegoStackingVision` | `peg_focus_view` | which colour is the solid-bottom brick | |
| | `image_findsoda.hdf5` | **Find-Soda** | `FindSodaVision` | `drawerview` | which drawers were already checked | |
| | `image_opendoors.hdf5` | **Open-Door** | `OpenDoorsVision` | `doorview` | which doors were already tried | |
|
|
| One robomimic-style hdf5 of teleoperated demonstrations per task (100 demos each). |
|
|
| Environments, teleoperation scripts and the CAMP training code are in the |
| [CAMP repository](https://robo-camp.github.io/) (`memory_manip_bench` package). |
|
|
| ## Download |
|
|
| ```bash |
| pip install -U "huggingface_hub[cli]" |
| huggingface-cli download harrywang01/Memory-Manip-Bench --repo-type dataset \ |
| --include image_uncoverblock.hdf5 --local-dir data/ |
| ``` |
|
|
| ## Data format |
|
|
| ``` |
| data/ (attrs: env_args — env_name + env_kwargs to rebuild the env) |
| └── demo_<i>/ (attrs: num_samples, model_file; Find-Soda also ep_meta) |
| ├── obs/<task camera>_image (T, 256, 256, 3) uint8 |
| ├── obs/robot0_eye_in_hand_image (T, 256, 256, 3) uint8 |
| ├── obs/robot0_eef_pos (T, 3) float |
| ├── obs/robot0_eef_quat (T, 4) float |
| ├── obs/robot0_gripper_qpos (T, 2) float |
| ├── actions (T, 7) delta OSC command |
| ├── actions_abs (T, 7) absolute [goal_pos(3), goal_axis_angle(3), gripper(1)] |
| ├── rewards, dones (T,) |
| └── states (T, D) MuJoCo states |
| ``` |
|
|
| ```python |
| import h5py, json |
| with h5py.File("data/image_uncoverblock.hdf5", "r") as f: |
| env_args = json.loads(f["data"].attrs["env_args"]) |
| demo = f["data/demo_1"] |
| imgs, acts = demo["obs/agentview_image"][:], demo["actions_abs"][:] |
| ``` |
|
|
| `model_file` (the demo's MuJoCo model) and `states` replay a demo in the environment. Find-Soda's |
| placement target is a model parameter, not simulator state, so its demos also carry |
| `ep_meta = {"target_xy", "soda_drawer_idx"}`, which `FindSodaVision.set_ep_meta` restores on replay. |
|
|
| ## Changelog |
|
|
| - **2026-10-11, `image_findsoda.hdf5`:** each demo's `model_file` now places the target patch where |
| the images show it (under the placed can), and `ep_meta` was added. Before, `model_file` held an |
| unrelated patch position, so replaying a demo from its states showed the can next to the patch. |
| Images, states and actions are unchanged. `media/find_soda.gif` was re-rendered. |
| |
| ## 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. |
| |
