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metadata 
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

Viewer · Paper

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

To be added.

Changelog

This is version 1.0. Corrections to the data are recorded in CHANGELOG.md.