An Innovative Approach: AI Robotic Learning Approach in Listening, Distinguishing, and Motion Learning

Robotic systems face difficulties in adapting and engaging cognitively within changing environments. They require advancements in learning methods that enable the system to process sensory data, recognize contextual cues, and execute precise movements. This article reviews various robotic learning techniques, with a focus on learning frameworks that help robots listen, interpret signals, and develop motion skills. It covers both discrete and continuous robotic arms, combining model-free and model-based learning strategies supported by machine learning and reinforcement learning. This discussion highlights our innovative method, cognitive distinguish listening approaches, and tentative volume scales.