Biologically-Inspired Learning for Humanoid Robots

Lecturer (assistant)
Duration4 SWS
TermSommersemester 2017
Language of instructionEnglish
Position within curriculaSee TUMonline
DatesSee TUMonline


Admission information

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

Teaching and learning methods

"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."

Recommended literature

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