Datasets:

serendipity800
/

ESRP-PD

pretty_name: ESRP-PD
task_categories:
  - robotics
  - reinforcement-learning
language:
  - en
tags:
  - embodied-ai
  - rearrangement
  - indoor-scenes
  - omnigibson
  - 3d-front
  - imitation-learning
  - benchmark
size_categories:
  - 1K<n<10K
viewer: false

ESRP-PD: Paired Scene Data for Embodied Scene Rearrangement Planning

Scene pairs and expert demonstrations for Embodied Scene Rearrangement Planning (ESRP), published at IEEE Robotics and Automation Letters (RA-L), 2026.

Project page · Code · Built on OmniGibson with scenes from 3D-FRONT

In ESRP, an embodied agent rearranges furniture in a fully 3D indoor scene to match a target layout given as a single top-down image, using only egocentric RGB observations. This dataset provides the initial-target scene pairs the agent is evaluated on, the train/validation/test splits used in the paper, and an example set of expert demonstrations for imitation learning.

5,549 scene pairs across bedrooms, living rooms, dining rooms, libraries, and other room types, holding more than 8,100 movable furniture items.

Files

File	Size	Contents	Needed for
`data.zip`	27.5 GB	`data/3d_front/scenes/` (the scene pairs) and `data/3d_front/usd_objects/` (converted furniture USD models), plus `data/assets/`	Running the simulator: every baseline
`train_data.txt`, `valid_data.txt`, `test_data.txt`	313 KB	The split lists, one scene name per line (also bundled as `data_splits.zip`)	Every baseline
`imitation_example.zip`	83 MB	`imitation_example/*.npz`, one expert demonstration per scene	ESRP-BC (imitation learning)
`3d_front.zip`	3.7 GB	`3D-FRONT/*.json`, the raw 3D-FRONT scene descriptions	Only if you regenerate scene pairs from scratch

Download everything, or just the parts you need:

pip install -U huggingface_hub

# Everything (~31 GB)
hf download serendipity800/ESRP-PD --repo-type dataset --local-dir ESRP-PD

# Just what you need to run the benchmark
hf download serendipity800/ESRP-PD data.zip train_data.txt valid_data.txt test_data.txt \
    --repo-type dataset --local-dir ESRP-PD

On older versions of the client, hf download is called huggingface-cli download.

Setup

Install ESRP-Bench first by following INSTALL.md, then:

Unzip data.zip and move the resulting data/ folder into the omnigibson/ package directory, so that you end up with omnigibson/data/3d_front/ and omnigibson/data/assets/. If you keep the data somewhere else, point ThreeD_FRONT_DATASET_PATH in omnigibson/macros.py at it instead.
Place train_data.txt, valid_data.txt, and test_data.txt into omnigibson/data/3d_front/. Some scripts read the lists relative to the repository root, so keep a copy in the directory above omnigibson/ as well.
Optional, for imitation learning: unzip imitation_example.zip into a directory named imitation_data/ alongside omnigibson/, then split it with python -m omnigibson.baseline.IL.split_scenes.
Build the mesh materials once, before running anything else:
```
python -m omnigibson.create_mesh
```

Scene pair format

Every scene lives in its own directory under data/3d_front/scenes/, named as the 3D-FRONT scene UID followed by the room, for example 00110bde-f580-40be-b8bb-88715b338a2a_LivingDiningRoom-44785:

File	Role
`<scene>_target.json`	Goal layout: the object poses the agent has to reproduce
`<scene>_initial.json`	Start layout: disarranged poses, plus a `target_bbox` for every rearrangeable object
`<scene>_target.png`	Top-down render of the goal layout, resized to 128×128 and given to the agent
`robot_pos_ori.txt`	Robot spawn position and orientation

Both JSON files follow OmniGibson's scene-state format. Objects carry an is_to_rearrange flag marking the ones the agent has to move; placement is judged against the target_bbox stored in the initial file. The task loads the initial layout and derives the goal by swapping initial for target in the filename, so the two files must stay side by side with matching names.

Splits

Split	Scenes	File
Train	4,995	`train_data.txt`
Validation	200	`valid_data.txt`
Test	354	`test_data.txt`

Each file is one scene directory name per line. Difficulty is defined by how many objects have to be rearranged: easy (1 object), medium (2 to 3), and hard (4 to 6).

About the demonstrations

imitation_example.zip is a proof-of-concept subset, not the full demonstration set used to train the baseline in the paper. It is enough to get a training run working end to end. To train a policy that reproduces the reported numbers, generate demonstrations yourself with the data generation pipeline described in DATASET.md.

Citation

@article{chen2026esrp,
  title     = {Embodied Scene Rearrangement Planning},
  author    = {Chen, Canzhi and Wang, Zan and Zhu, Siqi and Wu, Qi and Li, Yixuan and Liang, Wei},
  journal   = {IEEE Robotics and Automation Letters},
  year      = {2026},
  publisher = {IEEE}
}

Contact

Please open an issue in the code repository for questions about the dataset or the benchmark.