Musculoskeletal Humanoid

Musculoskeletal humanoid robotics aims to create robots with human-like bodies and control systems, focusing on achieving dexterity, robustness, and adaptability comparable to humans. Current research emphasizes developing advanced control strategies using neural networks, particularly recurrent neural networks and autoencoders, to manage redundancy, handle variable stiffness, and learn complex intersensory relationships within the robot's body. This work is significant for advancing robotics capabilities in manipulation, locomotion, and environmental interaction, potentially leading to more versatile and adaptable robots for various applications. The development of accurate, online learning methods for body schema acquisition and control is a key focus, enabling robots to adapt to changing conditions and handle unexpected events.