license: cc-by-nc-4.0
pretty_name: EmbRACE
language:
- en
tags:
- embodied-ai
- vision-language-navigation
- egocentric
- unreal-engine
- simulation
- benchmark
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: dataset
path: data/dataset.parquet
- split: benchmark
path: data/benchmark.parquet
EmbRACE: Embodied Reasoning and Action in Complex Environments
EmbRACE consists of two parts. The dataset is a set of 3,421 human demonstrations for closed-loop tasks in 55 environments, 48,264 steps in all, in which every step is paired with a rationale written from the agent's viewpoint and verified by annotators. The benchmark is a set of 686 tasks in 7 further environments, run in the closed loop and scored on the agent's final position and on the door and object states.
| Part | Environments | Tasks | Steps | Rationales |
|---|---|---|---|---|
| dataset | 55 | 3,421 | 48,264 | 48,264 |
| benchmark | 7 | 686 | 10,552 | 0 |
| Type | Task type | dataset | benchmark |
|---|---|---|---|
| 0 | Basic | 646 | 116 |
| 1 | Exploration | 591 | 117 |
| 2 | Dynamic Spatial-Semantic | 686 | 114 |
| 3 | Multi-stage | 644 | 106 |
| 4 | Open Door | 226 | 109 |
| 5 | Pick & Drop | 628 | 124 |
- Basic: The target is visible from the start pose and directly reachable.
- Exploration: The target is out of view at the start, so the agent searches the environment and commits to a direction before the target comes into view.
- Dynamic Spatial-Semantic: The target is visible but specified relationally or ordinally, for example "the second chair from the left". The relation is defined in the initial view.
- Multi-stage: The task names two targets to be reached in order.
- Open Door: The agent opens a door. Success is judged on the door state.
- Pick & Drop: The agent picks up an object and releases it beside a landmark. Success is judged on the object position.
Files
dataset/task/<Env>/<Task>.json task files
dataset/trajectory/<Env>/<Task>/ frames and record of each demonstration
benchmark/task/<Env>/<Task>.json task files read by the evaluation code
benchmark/trajectory/<Env>/<Task>/ reference human demonstration of each task
benchmark/success_region/<Env>/<Task>/ success regions of each task
packed/{dataset,benchmark}/<Env>.tar all files of one environment in one archive
data/{dataset,benchmark}.parquet one row per task with its initial frame
index/web/tasks.json task index read by the viewer
environments.json the 62 environments
pd_objects.json the ten Pick & Drop objects
SHA256SUMS checksums of all files
This is the layout that the archives in packed/ hold and that the evaluation code reads. The files shown on this page are a copy for browsing, in which every task folder and task file carries the prefix task_, for example benchmark/trajectory/CourtYard/task_x001312_y007194_z000118_t0_a/, because the Hub does not accept some of the original names. SHA256SUMS lists every file under its name in the archives.
Download
The 62 archives hold every file under dataset/ and benchmark/. Extracting them in one folder gives the layout above.
hf download mxlin043/EmbRACE --repo-type dataset --local-dir EmbRACE \
--include "packed/*" --include "environments.json" --include "pd_objects.json"
cd EmbRACE && for f in packed/*/*.tar; do tar -xf "$f"; done
One environment can also be downloaded on its own, for example
hf download mxlin043/EmbRACE --repo-type dataset --local-dir EmbRACE --include "packed/benchmark/CourtYard.tar"
The task table loads with the datasets library.
from datasets import load_dataset
tasks = load_dataset("mxlin043/EmbRACE", split="dataset")
Running the benchmark requires the UnrealZoo UE5.6 package of version v3.0.2 and the evaluation code.
Format
Task files
| Field | Content |
|---|---|
Instruction |
the instruction |
Type |
the task type, 0 to 5 |
Start_Pose |
position and orientation of the agent at the start, [x, y, z, pitch, yaw, roll] |
PD_ID, PD_Size |
Pick & Drop only, the object and its scale |
PD_Type |
Pick & Drop only, pd when the object is released beside a landmark and p when the task ends with the object held |
PD_Start_Pose |
Pick & Drop only, the pose at which the object is placed |
Exposure |
optional, an exposure offset for rendering that replaces the default of the environment |
A task is named by its start position in centimeters, its type and a letter, for example x001312_y007194_z000118_t0_a. The names of Pick & Drop tasks end in _p or _pd following PD_Type.
Trajectory records
Each demonstration is one folder with its frames 000.jpg, 001.jpg, … at 640×480 and the record infos.json. The frames are the egocentric frame before every action and the frame after the last one, so a demonstration of n actions has n+1 frames. The pose and state streams have n+1 entries aligned with the frames. Poses are given in the coordinate frame of the environment, in centimeters and degrees.
| Field | Content |
|---|---|
Instruction, Type, Start_Pose, PD_ID, PD_Size, PD_Type, PD_Start_Pose |
as in the task file |
Action |
the demonstrated actions (n) |
Pose_Trajectory |
the agent pose (n+1) |
Door_State_Trajectory |
Open Door only, the door state, 0 closed and 1 open (n+1) |
PD_Pose_Trajectory |
Pick & Drop only, the object pose (n+1) |
PD_Dist_2D |
Pick & Drop only, the horizontal distance from the agent to the object (n+1) |
Rationale |
dataset only, the rationale of each step (n) |
The actions are MoveForward, MoveBackward, TurnLeft, TurnRight, LookUp, LookDown, OpenDoor, Pick, Drop, MidwayTarget and Finish. MidwayTarget declares arrival at the first target of a Multi-stage task.
Success regions
Each benchmark task has an area.json with the regions it is scored against. Two images show the regions, xy_region.png as a top-down plot and ue_top_down_overlay.jpg drawn over the scene. Multi-stage tasks have one pair per target, prefixed midway_ and final_.
| Field | Content |
|---|---|
coordinate_system |
the frame of the geometry, the XY plane of the environment in centimeters |
distance_metric |
distances are measured in the XY plane |
stages |
one entry per target, final and for Multi-stage tasks also midway |
character_regions |
the polygons in which the agent has to stand |
target_footprints |
the footprint of the target |
character_reference_z |
the reference height of the agent at the target |
pd_object_regions, pd_object_reference_z |
Pick & Drop only, the region and the reference height of the object |
shortest_path_uu |
the reference length of SPL, in centimeters |
Other files
environments.json gives each environment its identifier, its name in the paper, its part and its setting (id, name, split, setting). pd_objects.json maps PD_ID to the name of the object. The Parquet tables in data/ hold one row per task with its environment, type, instruction, initial frame, actions and rationales.
License
The data are released under the Creative Commons Attribution-NonCommercial 4.0 license, for research use with attribution. The environments are those of UnrealZoo and are obtained from its release under its terms.
Citation
@article{lin2025embrace,
title={EmbRACE: Embodied Reasoning and Action in Complex Environments},
author={Lin, Mingxian and Huang, Wei and Li, Yitang and Jiang, Chengjie and Wu, Kui and Zhong, Fangwei and Chen, Weikai and Qian, Shengju and Wang, Xin and Qi, Xiaojuan},
journal={arXiv preprint arXiv:2507.10548},
year={2025}
}
Changelog
This is version 1.0. Corrections to the data are recorded in CHANGELOG.md.