mlsanityData to 7 Oct 2026

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

arXiv 2503.07340 ↗First posted 10 Mar 2025cs.RO cs.AI cs.LGcited by 6

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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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 13 of its 26 references that are in the map. First posted on arXiv 10 Mar 2025.

Builds on

13 of 26 references.

References · 13

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  5. 2024Research on Effectiveness Evaluation and Optimization of Baseball Teaching Method Based on Machine Learningcited by 8
  6. 2025Deep Semantic Graph Learning via LLM based Node Enhancementcited by 8
  7. 2024Accurate Prediction of Temperature Indicators in Eastern China Using a Multi-Scale CNN-LSTM-Attention modelcited by 7
  8. 2024Comparative Analysis of Listwise Reranking with Large Language Models in Limited-Resource Language Contextscited by 7
  9. 2025A 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 fieldcited by 6
  10. 2024Improving Academic Skills Assessment with NLP and Ensemble Learningcited by 5
  11. 2025Zero-Shot End-to-End Relation Extraction in Chinese: A Comparative Study of Gemini, LLaMA and ChatGPTcited by 5
  12. 2024Optimization of Pressure Management Strategies for Geological CO2 Sequestration Using Surrogate Model-based Reinforcement Learningcited by 4
1 more
  1. 2024Optimizing Load Scheduling in Power Grids Using Reinforcement Learning and Markov Decision Processescited by 2

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Citing papers per year: 2025: 6.

6 papers.

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  6. 2025Application of YOLOv8 in monocular downward multiple Car Target detectioncited by 1