Unveiling the Future: A Comprehensive Review of Machine Learning, Deep Learning, Multi-model Models and Explainable AI in Robotics

The rapid advancements in artificial intelligence (AI) have significantly transformed the fields of machine learning, deep learning, and robotics. This comprehensive review aims to provide an in-depth survey of the current state of AI models, focusing on machine learning, deep learning, large language models (LLMs), and multimodal models. Special emphasis is placed on explainable AI (XAI) techniques, which are crucial for fostering transparency, trust, and collaboration between humans and robots. We begin by categorizing and summarizing the various machine learning and deep learning models, including supervised, unsupervised, ensemble, and reinforcement learning approaches. We also explore the latest developments in LLMs and multimodal models, highlighting their applications and potential in various domains. The core of this review delves into XAI techniques, discussing both model-specific and model-agnostic methods. We examine global and local explainability, intrinsic and post-hoc explanations, and their relevance to different AI applications. Moreover, we extend our discussion to explainable AI in robotics, covering topics such as human-robot interaction, autonomous robot transparency, interpretable learning for robotic control, collaborative robotics, safety, adaptability, and ethical considerations. By integrating these diverse perspectives, this review aims to provide a holistic understanding of the interplay between advanced AI models and explainability. Our goal is to highlight the importance of transparent and interpretable AI systems in enhancing the functionality and reliability of robotic applications, ultimately contributing to the development of trustworthy AI technologies.