File size: 4,772 Bytes
a4d8742 75b6e78 a4d8742 75b6e78 a4d8742 75b6e78 a4d8742 9e5e6ed f0deedf 9e5e6ed 5c07d8f 9e5e6ed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 |
---
pretty_name: ENACT
language:
- en
task_categories:
- visual-question-answering
configs:
- config_name: default
data_files:
- QA.zip
dataset_info:
features:
- name: id
dtype: string
- name: type
dtype: string
- name: task_name
dtype: string
- name: key_frame_ids
sequence: string
- name: images
sequence: string
- name: question
dtype: string
- name: options
sequence: string
- name: gt_answer
sequence: int32
license: mit
tags:
- agent
size_categories:
- 1K<n<10K
---
# ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction
ENACT is a benchmark dataset for evaluating **embodied cognition** in vision–language models via **egocentric world modeling**. It probes whether models can reason about how the world changes under sequences of actions, using long-horizon household activities in a mobile manipulation setting.
- **Project page:** https://enact-embodied-cognition.github.io/
- **Code & evaluation:** https://github.com/mll-lab-nu/ENACT
- **Paper** https://arxiv.org/abs/2511.20937
## Dataset Summary
Each ENACT example is a **multi-image, multi-step reasoning problem** built from robot trajectories:
- **Forward world modeling**
- Input: one **current state image**, several **future state images** (shuffled), and a list of **actions in correct order**.
- Task: output a Python list of integers giving the **correct chronological order of future images** (e.g., `[1, 3, 2]`).
- **Inverse world modeling**
- Input: an **ordered sequence of images** showing state changes, plus **actions in shuffled order**.
- Task: output a Python list of integers giving the **correct chronological order of actions** (e.g., `[2, 3, 1]`).
All images are egocentric RGB observations rendered from long-horizon household tasks (e.g., assembling gift baskets, bringing water, preparing lunch boxes, cleaning up a desk).
## File Structure
After unpacking, the dataset has the following structure:
```text
.
├── enact_ordering.jsonl # All QA examples (one JSON per line)
└── images/
├── forward_world_modeling_3_steps/
├── forward_world_modeling_4_steps/
├── ...
├── forward_world_modeling_10_steps/
├── inverse_world_modeling_3_steps/
├── ...
└── inverse_world_modeling_10_steps/
````
Each task folder (e.g., `forward_world_modeling_3_steps/`) contains one subfolder per activity, such as:
```text
images/forward_world_modeling_3_steps/
├── assembling_gift_baskets_1749468508582193/
├── bringing_water_1750844141719178/
├── ...
```
Inside each activity folder are the PNGs for that trajectory (current state and future states, or ordered states in the inverse setting).
## JSONL Format
Each line in `enact_ordering.jsonl` is a JSON object:
```json
{
"id": "assembling_gift_baskets_1749468508582193_forward_world_modeling_3_steps_cfbcc15c",
"type": "forward_world_modeling_3_steps",
"task_name": "assembling_gift_baskets_1749468508582193",
"key_frame_ids": ["4150", "11360", "11834"],
"images": [
"QA/images/forward_world_modeling_3_steps/..._cur_state.png",
"QA/images/forward_world_modeling_3_steps/..._next_state_1.png",
"QA/images/forward_world_modeling_3_steps/..._next_state_2.png"
],
"question": "...natural language instructions and actions...",
"options": [],
"gt_answer": [1, 2]
}
```
* **`id`** – unique identifier for this QA instance.
* **`type`** – question type and horizon, e.g. `forward_world_modeling_3_steps` or `inverse_world_modeling_4_steps`.
* **`task_name`** – underlying household task instance.
* **`key_frame_ids`** – frame indices of selected key frames in the trajectory.
* **`images`** – relative paths to PNG images:
* index 0 is the **current state**;
* subsequent entries are **future states** (forward) or later states (inverse).
* **`question`** – natural language prompt specifying the task setup, actions, and the required output as a Python list of integers.
* **`gt_answer`** – ground-truth ordering of image or action labels (list of integers, e.g. `[1, 3, 2]`).
## Usage
To evaluate, follow the scripts in the code repository: [https://github.com/mll-lab-nu/ENACT](https://github.com/mll-lab-nu/ENACT)
## Citation
If you use ENACT, please cite the paper:
```
@article{wang2025enact,
title={ENACT: Evaluating Embodied Cognition with World Modeling of Egocentric Interaction},
author={Wang, Qineng and Huang, Wenlong and Zhou, Yu and Yin, Hang
and Bao, Tianwei and Lyu, Jianwen and Liu, Weiyu and Zhang, Ruohan
and Wu, Jiajun and Li, Fei-Fei and Li, Manling},
journal={arXiv preprint arXiv:2511.20937},
year={2025}
}
``` |