Machine Learning Techniques for Socially Intelligent Robots

Abstract

This article mainly describes the machine learning technology of socially intelligent robots. First, the history of robot development is introduced: from the very beginning for repetitive and dangerous tasks in industrial environments to the emergence of artificial intelligence and machine learning, the development of robots to give greater autonomy and adaptability. Secondly, it begins to elaborate the concept of socially intelligent robots, which refers to machines with advanced capabilities that interact and communicate with humans in a socially adept way, and use complex sensors, cameras and algorithms to perceive the social environment around them. Following the introduction of machine learning in the mid-20th century by Alan Turing, to the 1950s and 1960s began to simulate the way the brain processes information, and then symbolic reasoning systems, with the emergence of vector machines and integrated methods, deep learning technology further development, machine learning reached a breakthrough. It then begins with an introduction to the concepts of machine learning theory: understanding the principles, algorithms, and foundations of mathematical frameworks that drive the development and application of machine learning models, consisting of statistical learning theory, computational learning theory, information theory, and Bayesian learning. It then elaborates on recent common deep learning techniques: Converters for natural language processing - implement more efficient models using self-attention mechanisms to capture dependencies between different words in a sequence, self-supervised learning training techniques for deep learning models that do not rely on labeled data sets, training models to predict certain parts of other input data, and meta-learning training models to quickly adapt to new tasks with minimal data. It then introduces machine learning techniques for socially intelligent robots: Emotion recognition and response - enabling robots to interpret human emotions through recognition such as facial expressions, voice tone and body language, natural language processing - socially intelligent robots to understand and generate human language using NLP technology, adaptive learning and personalization - enabling socially intelligent robots to adapt and personalize their interactions based on personal preferences and past experiences, Gesture and posture recognition - Uses machine learning models to recognize and interpret human gestures and posture. Finally, it summarizes the synergies of machine learning for socially intelligent robots, solving ethical problems, establishing more advanced technical solutions and harmonious and meaningful human-computer interaction.