# Embodied Image Quality Assessment for Robotic Intelligence

arXiv: https://arxiv.org/abs/2412.18774 · HTML: https://mlsanity.com/paper/2412.18774/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 69 references that are in the map.
- First posted on arXiv 25 Dec 2024.

## Builds on (most cited first, 30 of 35)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [AI2-THOR: An Interactive 3D Environment for Visual AI](https://mlsanity.com/paper/1712.05474/) (2017)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [Path Aggregation Network for Instance Segmentation](https://mlsanity.com/paper/1803.01534/) (2018)
- [SAPIEN: A SimulAted Part-based Interactive ENvironment](https://mlsanity.com/paper/2003.08515/) (2020)
- [MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2204.08958/) (2022)
- [TD-MPC2: Scalable, Robust World Models for Continuous Control](https://mlsanity.com/paper/2310.16828/) (2023)
- [KonIQ-10k: An ecologically valid database for deep learning of blind image quality assessment](https://mlsanity.com/paper/1910.06180/) (2019)
- [Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network](https://mlsanity.com/paper/1907.02665/) (2019)
- [ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI](https://mlsanity.com/paper/2410.00425/) (2024)
- [TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment](https://mlsanity.com/paper/2308.03060/) (2023)
- [Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment](https://mlsanity.com/paper/1612.01697/) (2016)
- [Massive Online Crowdsourced Study of Subjective and Objective Picture Quality](https://mlsanity.com/paper/1511.02919/) (2015)
- [Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index](https://mlsanity.com/paper/1308.3052/) (2013)
- [Aligning Cyber Space with Physical World: A Comprehensive Survey on Embodied AI](https://mlsanity.com/paper/2407.06886/) (2024)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment](https://mlsanity.com/paper/2004.05508/) (2020)
- [AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment](https://mlsanity.com/paper/2306.04717/) (2023)
- [Attentions Help CNNs See Better: Attention-based Hybrid Image Quality Assessment Network](https://mlsanity.com/paper/2204.10485/) (2022)
- [Perceptual Image Quality Assessment with Transformers](https://mlsanity.com/paper/2104.14730/) (2021)
- [Learning Conditional Knowledge Distillation for Degraded-Reference Image Quality Assessment](https://mlsanity.com/paper/2108.07948/) (2021)
- [A-Bench: Are LMMs Masters at Evaluating AI-generated Images?](https://mlsanity.com/paper/2406.03070/) (2024)
- [Quality-Aware Image-Text Alignment for Opinion-Unaware Image Quality Assessment](https://mlsanity.com/paper/2403.11176/) (2024)

## Built on by (most cited first, 2 of 2)

- [RGC-VQA: An Exploration Database for Robotic-Generated Video Quality Assessment](https://mlsanity.com/paper/2506.23852/) (2025)
- [Distortion-Resilient Robotic Imitation Learning for Autonomous Cable Routing](https://mlsanity.com/paper/2606.11577/) (2026)

## Abstract (arXiv)

Image Quality Assessment (IQA) of User-Generated Content (UGC) is a critical technique for human Quality of Experience (QoE). However, does the the image quality of Robot-Generated Content (RGC) demonstrate traits consistent with the Moravec paradox, potentially conflicting with human perceptual norms? Human subjective scoring is more based on the attractiveness of the image. Embodied agent are required to interact and perceive in the environment, and finally perform specific tasks. Visual images as inputs directly influence downstream tasks. In this paper, we explore the perception mechanism of embodied robots for image quality. We propose the first Embodied Preference Database (EPD), which contains 12,500 distorted image annotations. We establish assessment metrics based on the downstream tasks of robot. In addition, there is a gap between UGC and RGC. To address this, we propose a novel Multi-scale Attention Embodied Image Quality Assessment called MA-EIQA. For the proposed EPD dataset, this is the first no-reference IQA model designed for embodied robot. Finally, the performance of mainstream IQA algorithms on EPD dataset is verified. The experiments demonstrate that quality assessment of embodied images is different from that of humans. We sincerely hope that the EPD can contribute to the development of embodied AI by focusing on image quality assessment. The benchmark is available at https://github.com/Jianbo-maker/EPD_benchmark.