Motor Control

Motor control research focuses on understanding how organisms generate and regulate movement, aiming to replicate these capabilities in robots. Current efforts concentrate on developing robust and adaptable control policies, often inspired by biological systems like the central pattern generator and incorporating hierarchical architectures and attention mechanisms to handle complex tasks and morphological variations. These advancements leverage neural networks, including distributed models and generative models, to learn effective control strategies from interaction with the environment, improving robot dexterity and locomotion. This work has significant implications for robotics, enabling more versatile and adaptable robots capable of performing complex tasks in unstructured environments.