Motor Skill

Motor skill acquisition and control are active research areas focusing on understanding how humans and robots learn and execute complex movements. Current research employs diverse approaches, including reinforcement learning algorithms (often with hierarchical structures or multi-task learning), imitation learning, and the development of novel neural network architectures (like 3DCNN ResNets and specialized networks for EEG decoding) to analyze and model motor skills. These advancements have implications for diverse fields, such as assessing developmental delays in toddlers, improving rehabilitation therapies for neurological disorders like Parkinson's disease, and enabling more agile and adaptable robots capable of performing complex tasks.