Mit And Stanford Researchers Developed A Machine-learning Technique That Can Efficiently Learn To Control A Robot, Leading To Better Performance With Less Data

Researchers from MIT and Stanford University have developed a new machine-learning technique that has the potential to revolutionize the control of robots in dynamic environments. The technique integrates principles from control theory into the machine learning process, allowing for the creation of more efficient and effective controllers. The method draws inspiration from how roboticists utilize physics to derive simpler robot models, and incorporates control-oriented structures during machine learning to extract controllers directly from the learned dynamics model.