README.md

---
pretty_name: PRICE
license: apache-2.0
language:
- en
size_categories:
- n<1K
task_categories:
- image-to-image
tags:
- image-generation
- image-to-image
- robotics
- embodied-ai
- benchmark
configs:
- config_name: default
  drop_labels: true
---

# PRICE: Prediction of Real-world Interactions with Constraints Evaluation

PRICE-V0.1 is a benchmark for instruction-conditional image-to-image generation grounded in real-world robot, egocentric, and third-person interaction data.

Given an initial-state image and a natural-language instruction, a model predicts the target-state image resulting from the instructed action. The benchmark evaluates instruction following, scene consistency, and physical plausibility.

The evaluation code and protocol live in [`evaluation/image_gen/PRICE/` in orca-wm/Orca](https://github.com/orca-wm/Orca/tree/main/evaluation/image_gen/PRICE).

## Benchmark overview

Each PRICE example pairs a natural-language instruction and an initial-state image with a reference target-state image. The examples below illustrate the benchmark's diverse robot and egocentric interaction scenes, viewpoints, and action outcomes.

[![PRICE benchmark examples](https://raw.githubusercontent.com/orca-wm/Orca/main/assets/PRICE_Overview.png)](https://github.com/orca-wm/Orca/blob/main/assets/PRICE_Overview.png)

*Examples from PRICE. Each pair shows the initial state and the target state corresponding to the instruction.*

## Dataset structure

The dataset contains one `test` split with 100 samples and the following principal columns:

<div style="display: flex; align-items: flex-start; gap: 16px; flex-wrap: wrap;">
<table style="flex: none; width: auto; min-width: 0; margin: 0;">
<thead>
<tr><th align="center">Column</th><th align="center">Type</th><th align="center">Description</th></tr>
</thead>
<tbody>
<tr><td align="center"><code>id</code></td><td align="center">string</td><td align="center">Stable PRICE sample identifier.</td></tr>
<tr><td align="center"><code>query</code></td><td align="center">image</td><td align="center">Initial-state image.</td></tr>
<tr><td align="center"><code>output</code></td><td align="center">image</td><td align="center">Reference target-state image.</td></tr>
<tr><td align="center"><code>lang</code></td><td align="center">string</td><td align="center">Natural-language action instruction.</td></tr>
<tr><td align="center"><code>dataset</code></td><td align="center">string</td><td align="center">Source collection identifier.</td></tr>
</tbody>
</table>

<table style="flex: none; width: auto; min-width: 0; margin: 0;">
<thead>
<tr><th align="center">Source</th><th align="center">Samples</th></tr>
</thead>
<tbody>
<tr><td align="center"><code>agibot_world</code></td><td align="center">30</td></tr>
<tr><td align="center"><code>homeinteract</code></td><td align="center">20</td></tr>
<tr><td align="center"><code>pe_video</code></td><td align="center">20</td></tr>
<tr><td align="center"><code>psi_ego</code></td><td align="center">30</td></tr>
<tr><td align="center"><strong>Total</strong></td><td align="center"><strong>100</strong></td></tr>
</tbody>
</table>
</div>

Additional provenance fields, timestamps, indices, random seed, and image checksums are retained from the source manifest.

## Usage

Load PRICE directly from Hugging Face:

```python
from datasets import load_dataset

dataset = load_dataset("BAAI/PRICE", split="test")
sample = dataset[0]

initial_image = sample["query"]
target_image = sample["output"]
instruction = sample["lang"]
```

Load the prepared folder locally:

```python
from datasets import load_dataset

dataset = load_dataset("imagefolder", data_dir="data", split="test")
```

## Evaluation

Use the evaluator and judge prompts from the GitHub repository. Model predictions should be named by the corresponding `id`, following the layout documented there.

## Data sources

PRICE-V0.1 contains selected samples derived from AgiBot World, HomeInteract, PE-Video, and PsiBot SynData. Consult the PRICE paper and GitHub repository for complete source citations and selection details.

## License

PRICE is released under the Apache License 2.0.

## Version

This layout corresponds to PRICE-V0.1. Tag the Hugging Face Dataset repository with `v0.1` and pin that revision from the evaluation code for reproducible results.