# Image Quality Assessment for Embodied AI

arXiv: https://arxiv.org/abs/2505.16815 · HTML: https://mlsanity.com/paper/2505.16815/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 69 references that are in the map.
- First posted on arXiv 22 May 2025.

## Built on by (most cited first, 4 of 4)

- [Static and Plugged: Make Embodied Evaluation Simple](https://mlsanity.com/paper/2508.06553/) (2025)
- [IQA-Spider: Unifying Multi-Granularity Image Quality Assessment with Reasoning, Grounding and Referring](https://mlsanity.com/paper/2605.24553/) (2026)
- [Just Noticeable Difference Modeling for Token Compression in Vision-Language-Action Models](https://mlsanity.com/paper/2608.21247/) (2026)
- [FireNav: Decision-Oriented Reconstruction Evidence Routing for Vision-Language Navigation in Indoor Fire Environments](https://mlsanity.com/paper/2610.15595/) (2026)

## Abstract (arXiv)

Embodied AI has developed rapidly in recent years, but it is still mainly deployed in laboratories, with various distortions in the Real-world limiting its application. Traditionally, Image Quality Assessment (IQA) methods are applied to predict human preferences for distorted images; however, there is no IQA method to assess the usability of an image in embodied tasks, namely, the perceptual quality for robots. To provide accurate and reliable quality indicators for future embodied scenarios, we first propose the topic: IQA for Embodied AI. Specifically, we (1) based on the Mertonian system and meta-cognitive theory, constructed a perception-cognition-decision-execution pipeline and defined a comprehensive subjective score collection process; (2) established the Embodied-IQA database, containing over 36k reference/distorted image pairs, with more than 5m fine-grained annotations provided by Vision Language Models/Vision Language Action-models/Real-world robots; (3) trained and validated the performance of mainstream IQA methods on Embodied-IQA, demonstrating the need to develop more accurate quality indicators for Embodied AI. We sincerely hope that through evaluation, we can promote the application of Embodied AI under complex distortions in the Real-world. Project page: https://github.com/lcysyzxdxc/EmbodiedIQA