IN2107 Seminar on Robotics Science and Systems Intelligence

Lecturer (assistant)
  • Sami Haddadin [L]
  • Riddhiman Laha
  • Srinivasan Lakshminarayanan
  • Kim Peper
  • Abdalla Swikir
TypeAdvanced seminar
Duration2 SWS
TermWintersemester 2022/23
Language of instructionEnglish
Position within curriculaSee TUMonline

Admission information


Upon completion of this course, students will be able to identify major research directions in robotics and artificial intelligence as well as to investigate and critically analyze relevant research topics independently. Moreover, they will be able to discuss/present the fundamental principles and topics in the field in an understandable way.


The focus of this course is current research topics in robotics and artificial intelligence. Students investigate the specific topics by referring to the relevant papers and present their results at a colloquium. The presented research papers deal with current trends in robotics, with specific various topics including: - Imitation learning, Bayesian/probabilistic learning, neural networks - Bio-inspired learning and control - State estimation, mapping - Multimodal perception and sensor fusion - Model identification - Motion planning - Motion and force control - Applications in manipulation, mobility, driving, flight, and space - Soft robotics - Robot hands - Manipulation and grasping - Rehabilitation and healthcare robotics - Humanoids - Physical human-robot interaction - Cognitive architectures - Computational learning theory - Evolutionary systems - Reinforcement learning - Space robotics - Robotic vision and perception


- Fundamentals of robotics (kinematics, dynamics, control) - Fundamentals of machine learning

Teaching and learning methods

Students are taught to read, present, and discuss research papers.


In this course, students must demonstrate their ability to review and synthesize robotics and AI research papers as well as to present and discuss them in the context of the fundamental principles and topics in the field. The module grade is based on the completeness and quality of the paper synthesis as well as the presentation. The module grade is based on the completeness and quality of a written report (80%) and presentation (20%).

Recommended literature

- The most famous conferences and journals in the area of robotics, AI and machine learning such as ICRA, IROS, NIPS, The International Journal of Robotics Research, IEEE Transactions on Robotics, etc. - B. Siciliano, O. Khatib, ‘Springer Handbook of Robotics’, Springer, 2016. - C. M. Bishop, ‘Pattern Recognition and Machine Learning’, Springer-Verlag New York. Inc., Secaucus, NJ, USA, 2006. - K. M. Lynch, ‘Modern Robotics-Mechanics, Planning, and Control: Video supplements and software’, 2017. - S. Russell, P. Norvig, ‘Artificial intelligence: a modern approach’, Pearson Education Limited, 2016. - S. Thrun, W. Burgard, D. Fox, ‘Probabilistic robotics’, MIT press, 2005. - S. Kajita, H. Hirukawa, K. Harada, K. Yokoi, ‘Introduction to Humanoid Robotics’, Springer, 2014. - A. Goswami, P. Vadakkepat, ‘Humanoid Robotics: A Reference’, Springer, 2018. - A. Ellery, ‘An introduction to space robotics’, Springer-Verlag New York, Inc., 2000.