# Research and Design on Intelligent Recognition of Unordered Targets for Robots Based on Reinforcement Learning

arXiv: https://arxiv.org/abs/2503.07340 · HTML: https://mlsanity.com/paper/2503.07340/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 26 references that are in the map.
- First posted on arXiv 10 Mar 2025.

## Builds on (most cited first, 13 of 13)

- [Deception Detection from Linguistic and Physiological Data Streams Using Bimodal Convolutional Neural Networks](https://mlsanity.com/paper/2311.10944/) (2023)
- [Exploring Diverse Methods in Visual Question Answering](https://mlsanity.com/paper/2404.13565/) (2024)
- [Robustness of Large Language Models Against Adversarial Attacks](https://mlsanity.com/paper/2412.17011/) (2024)
- [AltGen: AI-Driven Alt Text Generation for Enhancing EPUB Accessibility](https://mlsanity.com/paper/2501.00113/) (2024)
- [Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learning](https://mlsanity.com/paper/2411.15721/) (2024)
- [Deep Semantic Graph Learning via LLM based Node Enhancement](https://mlsanity.com/paper/2502.07982/) (2025)
- [Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention model](https://mlsanity.com/paper/2412.07997/) (2024)
- [Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contexts](https://mlsanity.com/paper/2412.20061/) (2024)
- [A CT Image Classification Network Framework for Lung Tumors Based on Pre-trained MobileNetV2 Model and Transfer learning, And Its Application and Market Analysis in the Medical field](https://mlsanity.com/paper/2501.04996/) (2025)
- [Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learning](https://mlsanity.com/paper/2403.07360/) (2024)
- [Improving Academic Skills Assessment with NLP and Ensemble Learning](https://mlsanity.com/paper/2409.19013/) (2024)
- [Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPT](https://mlsanity.com/paper/2502.05694/) (2025)
- [Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processes](https://mlsanity.com/paper/2410.17696/) (2024)

## Built on by (most cited first, 6 of 6)

- [Neural Pruning for 3D Scene Reconstruction: Efficient NeRF Acceleration](https://mlsanity.com/paper/2504.00950/) (2025)
- [Research on Personalized Financial Product Recommendation by Integrating Large Language Models and Graph Neural Networks](https://mlsanity.com/paper/2506.05873/) (2025)
- [A Narrative Review on Large AI Models in Lung Cancer Screening, Diagnosis, and Treatment Planning](https://mlsanity.com/paper/2506.07236/) (2025)
- [Industrial Internet Robot Collaboration System and Edge Computing Optimization](https://mlsanity.com/paper/2504.02492/) (2025)
- [Deep Learning Model Acceleration and Optimization Strategies for Real-Time Recommendation Systems](https://mlsanity.com/paper/2506.11421/) (2025)
- [Application of YOLOv8 in monocular downward multiple Car Target detection](https://mlsanity.com/paper/2505.10016/) (2025)

## Abstract (arXiv)

In the field of robot target recognition research driven by artificial intelligence (AI), factors such as the disordered distribution of targets, the complexity of the environment, the massive scale of data, and noise interference have significantly restricted the improvement of target recognition accuracy. Against the backdrop of the continuous iteration and upgrading of current AI technologies, to meet the demand for accurate recognition of disordered targets by intelligent robots in complex and changeable scenarios, this study innovatively proposes an AI - based intelligent robot disordered target recognition method using reinforcement learning. This method processes the collected target images with the bilateral filtering algorithm, decomposing them into low - illumination images and reflection images. Subsequently, it adopts differentiated AI strategies, compressing the illumination images and enhancing the reflection images respectively, and then fuses the two parts of images to generate a new image. On this basis, this study deeply integrates deep learning, a core AI technology, with the reinforcement learning algorithm. The enhanced target images are input into a deep reinforcement learning model for training, ultimately enabling the AI - based intelligent robot to efficiently recognize disordered targets. Experimental results show that the proposed method can not only significantly improve the quality of target images but also enable the AI - based intelligent robot to complete the recognition task of disordered targets with higher efficiency and accuracy, demonstrating extremely high application value and broad development prospects in the field of AI robots.