README.md

---
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}
}
```