Robot Dynamic

Robot dynamics research focuses on accurately modeling and controlling the movement of robots, aiming to improve their performance, safety, and adaptability in diverse environments. Current efforts concentrate on developing robust and efficient models, often employing neural networks (including Transformers and Graph Neural Networks), Bayesian methods, and model predictive control (MPC) algorithms to handle uncertainties and complex interactions. These advancements are crucial for enabling more sophisticated robot behaviors in applications ranging from warehouse automation and search and rescue to human-robot collaboration and legged locomotion.