Implementing Machine Learning for AI-Powered Solutions in Robotics, Computer Vision, and Natural Language Processing

• Published in: 2025 Global Conference in Emerging Technology (GINOTECH) pp. 1 - 6
• Main Authors: Kasu, Venugopala Reddy, Malamuthu, Bakkiyaraj Kanthimathi, Kumar, B. Shravan, Pandi, V. Samuthira, Sivajothi, E., Deepika, D S
• Format: Conference Proceeding
• Language: English
• Published: IEEE 09.05.2025
• Subjects: Autonomous Systems; Computational modeling; Computer architecture; Computer vision; Convolutional Neural Networks (CNNs); Data models; Human-Robot Interaction (HRI); Natural Language Processing (NLP); Real-time systems; Reinforcement learning; Reinforcement Learning (RL); Scalability; Sentiment analysis; Service robots; Transformer Models; Transformers; Vision Transformers (ViTs)
• DOI: 10.1109/GINOTECH63460.2025.11077035

Machine Learning (ML) is a relatively recent development that has had a profound impact on Robotics, Computer Vision (CV) and Natural Language Processing (NLP) by allowing automation of intelligent processing, perception and human-computer interaction. ML-driven AI solutions are crucial in these areas for real-time decision-making, pattern recognition, and autonomous operations; traditional rule-based methods tend to lack the adaptability and scalability to meet the demands of these fields. RL and deep learning have increasingly contributed to the improvement of robotic perception, motion planning, and manipulation in robotics, enabling better and more adaptive autonomous systems. Convolutional neural networks (CNNs) and transformer-based architectures have revolutionized computer vision, with applications ranging from object detection and image segmentation to facial recognition, allowing for advanced visual analysis across sectors such as healthcare, security, and self-driving cars. In NLP, the introduction of transformers like BERT and GPT has transformed the space by enabling more context-aware and human-like AI-driven language models, leading to drastic improvements in areas like speech recognition, sentiment analysis, and machine translation. This study delves into the application of ML techniques across these domains, evaluating the implications on efficiency, accuracy, and scalability along with challenges like data quality, model interpretability, and computational constraints. DQN has made advances for robotics, ViT in the domain of computer vision, and LLMs in the subject of NLU. A comparative study to analyse the progress and limitations of such state-of-the-art ML models. The results highlight the promise of multi-modal AI systems, where machine learning algorithms work in conjunction to improve robotic navigation, as well as visual perception and natural language understanding. Extending prior studies of intelligent automation, this research points the way to the development of new adaptive, human-centric, and autonomous AI innovations in diverse sectors.