See TUMonline
Note: -

"After this course, students are capable of: - Using the robot operating system (ROS) with the NAO robot. - Understand the main biological mechanisms responsible for learning. - Implement and evaluate biologically-inspired algorithms for sensory-motor mappings and reinforcement learning."

"1) Introduction - Motivation - Human brain research - Human brain inspiration in motor control 2) Learning - Why humanoids need to learn? - What humanoids need to learn? - Learning algorithms -- Supervised learning -- Unsupervised learning -- Reinforcement learning - Learning by self-exploration - Learning by demonstration 3) The cerebellum - Facts - Anatomy - Neural circuitry - Effects of cerebellar disease 4) Computational model of the cerebellum - Associative memory - Cerebellar model articulation controller (CMAC) 5) The basal ganglia - Anatomy and major components - Projections from and to other brain regions - Direct and indirect pathway - Basal ganglia loops 6) Reinforcement learning (RL) - Characteristics - Reward - Agent and environment - Major components of a RL agent - Temporal difference learning 7) Self-organizing maps (SOMs) 8) The central pattern generator (CPG) - Biological approach - Computational model - Multilayered CPG"

C/C++ programming skills

"Following teaching methods are used: - Lectures that provide the necessary theory for the tutorials. - Tutorials with laboratory assignments which ensure that major parts of the taught content, e.g. learning algorithms, are realized and tested on the robots."

R. S. Sutton and A. G. Barto: "Reinforcement learning: An introduction", MIT Press, 1998.