Advancements in Adaptive Autonomous Robotics: Enhancing Intelligence and Decision-Making in Unstructured Environments

Abstract

Artificial intelligence (AI) is revolutionizing robotics by enhancing how robots interact with, perceive, and navigate their environments1. Advanced machine learning models, natural language processing, and enhanced computer vision are expanding the possibilities of what robots can achieve, making them more adaptable, efficient, and autonomous1. Adaptive robotics further allows robots to develop skills autonomously through learning, enabling them to modify their behavior and function in response to contextual changes. AI-powered robots use machine learning techniques to understand and analyze their environment, learn from interactions with humans, and adapt their behavior accordingly, making them a key tool for use in collaborative environments. Multimodal Large Language Models (LLMs) are also transforming AI in robotics by enabling machines to process and understand diverse forms of input, such as text, images, and audio, leading to more informed decisions1. The integration of advanced image processing techniques and neural networks allows robots to interpret visual data more effectively, which is essential for a wide range of applications, from autonomous vehicles to healthcare devices. These advancements promise to improve functionality, efficiency, and safety across various applications. This paper examines the transformative role of AI and machine learning (ML) in enhancing robot decision-making, adaptability, and learning capabilities across various domains