Objectives and competences

Course Robotics gives an overview over the entire field of robotics. Topics are selected according to the needs of engineers who introduce or maintain robotic cells or production lines in industry. In the theoretical part of the course students learn the geometric model of the robot, which is essential for programming robots. In the practical part of the course, students in small groups learn programming of industrial robots.


Knowledge in mathematics, physics and electrical engineering in the first level od studies.


  1. Introduction of robots in industry
  2. Robot components
  3. Kinematics and dynamics of robot mechanisms
  4. Robot control and trajectory planning
  5. Task planning and robot programming
  6. Examples of industrial robot applications

Intended learning outcomes

Knowledge and understanding:
Knowledge of pose description with homogeneous transformation matrices, knowledge of geometric models of robot mechanisms, knowledge of control schemes that are specific to robotics. Link theoretical knowledge of geometric models with programming of industrial robots. Programming and working with industrial robots. Use of knowledge for development of robotic production cells.


• T. Bajd, M. Mihelj, J. Lenarčič, A. Stanovnik, M. Munih: Robotika, Založba FE in FRI, 2008. Catalogue
• M. Mihelj, T. Bajd, A. Ude, J. Lenarčič, A. Stanovnik, M. Munih, J. Rejc, S. Šlajpah: Robotics, Springer, 2019.


• The written exame is a seminar. Each student chooses a topic that s/he needs to review and present his/her understanding of the selected topic. • The oral exam assesses knowledge of the theoretical and general concepts presented through the lectures and exercises. This is related to the problems of the introduction of robot in industry, structure of robots (in particular industrial robots), functions of robots, design and control of robots, basic motion characteristics of robots, technological and economical aspects of robotization. 50/50

Lecturer's references

Dr. Aleš Ude is fully employeed at the Jožef Stefan Institute.

• Petrič, T., Gams, A., Colasanto, L., Ijspeert, A. J., & Ude, A. (2018). Accelerated Sensorimotor Learning of Compliant Movement Primitives. IEEE Transactions on Robotics, 34(6), 1636–1642.
• Nemec, B., Likar, N., Gams, A., & Ude, A. (2018). Human robot cooperation with compliance adaptation along the motion trajectory. Autonomous Robots, 42(5), 1023–1035.
• Gašpar, T., Nemec, B., Morimoto, J., & Ude, A. (2018). Skill learning and action recognition by arc-length dynamic movement primitives. Robotics and Autonomous Systems, 100, 225–235.
• Kramberger, A., Gams, A., Nemec, B., Chrysostomou, D., Madsen, O., & Ude, A. (2017). Generalization of orientation trajectories and force-torque profiles for robotic assembly. Robotics and Autonomous Systems, 98, 333–346.
• Abu-Dakka, F. J., Nemec, B., Jørgensen, J. A., Savarimuthu, T. R., Krüger, N., & Ude, A. (2015). Adaptation of manipulation skills in physical contact with the environment to reference force profiles. Autonomous Robots, 39(2), 199–217.