Tutor for Artificial Motor Learning

This course is concerned with methods of artificial intelligence that enable biomimetic motor learning in intelligent systems.

We consider a range of methods from systems-and-control methods to machine learning approaches, with a focus on data-driven learning control and model-based reinforcement learning. We discuss the core concepts ofthe methods, analyze and compare their potential and shortcomings, and apply them to example problems. The covered topics include but are not limited to:

• the role of motor learning in biological and AI systems
• definition and classification of motor learning tasks
• parametric and non-parametric models of motor dynamics
• learning control methods (model-based and data-based) for motor learning tasks
• reinforcement learning (model-free and model-based) for motor learning tasks
• advanced approaches from recent literature
• combination and implementation of methods
• stability, optimality, robustness and usability properties
• performance assessment in simulation and experiment The example problems to which we will apply the concepts and methods will stem from application domains in which artificial motor learning is considered crucial, such as robotics, neuroprosthetics and autonomous vehicles.