Human Intention

Understanding human intention is a crucial area of research aiming to enable more natural and effective human-robot interaction and improve AI systems' ability to interpret and respond to human needs. Current research focuses on developing models that infer intentions from various modalities, including visual cues (gaze, body language, actions), language, and sensor data, often employing machine learning techniques such as transformer networks, recurrent neural networks (RNNs, LSTMs), and particle filters. These advancements are significant for improving the safety and efficiency of robots in collaborative tasks, enhancing the usability of AI assistants, and providing insights into human behavior and decision-making processes.