I believe that one should explain things in a way that anyone can understand them. My vision of education is to strive to be able to explain very complicated concepts in a way that everyone can understand them (at least in a sense of the bigger picture). Once you have students’ attention and basic understanding, you can then always go into more complex explanations and underlying details afterwards.

My general education method follows a top-to-bottom approach, which means that I start with the big-picture concepts first. Traditionally robotics has been taught in a bottom-to-top approach, starting with the underlying details, such as deriving robot kinematic and dynamic models, and only at the end exploring the big picture concepts of how to actually control a robot to do something useful (e.g., move its hand to pick something). Of course, robot kinematic and dynamic models are essential for the underlying function of the robot, however, they are not essential in the big picture where we control the robot to do something useful. Therefore, I strive to reverse the order and teach about the big picture first and underlying details later, as one can still first learn how to use end-effector control to move the robot hand to pick some object if they are initially provided with kinematic and dynamic models and a brief explanation of their purpose on a higher level. After one learns how to make something useful with the robot (which is much more motivating and engaging than deriving underlying details), then one can also learn how to derive the details that were previously given to you (i.e., kinematic and dynamic models) with a much better understanding of why they are important.

I am also a strong proponent of making students work on practical problems with actual software and hardware in order to give them concrete motivation and challenge. In the end, I see the purpose of lectures only to tell students "what" and "why" should be learned, while the actual learning of "how" should be done by students themselves through working on practical problems. In other words, one can only properly learn some theory that was seen during the lectures after implementing it in practice. While implementing it once will lead to learning the theory, a crucial step beyond is implementing it many times in different situations, which will lead to experience.

RO47001 Robot Dynamics & Control [2020-present] (Master in Robotics, TU Delft), role: responsible instructor

• In this obligatory course, together with co-instructors (Barys Shyrokau and J. Micah Prendergast), we teach the fundamentals of robot dynamics and control. The course examines the theory of how to design different controllers and derive robot kinematics and dynamics models through three application areas: robotic manipulators (me), advanced manipulation (Micah), and mobile robots (Barys). The theoretical lectures are supplemented with interactive sessions and practical assignments where students have to implement the learnt theory in Phyton and Matlab.

RO47013 Control in Human-Robot Interaction [2020-present] (Master in Robotics, TU Delft), role: co-instructor

• In this advanced elective course, together with the responsible instructor (Michael Wiertlewski), we teach the principles related to the control of physical human-robot interaction. The underlying theory is grounded in the impedance control principle. The course examines several key application areas, such as human-robot collaboration, haptic and tactile interfaces, teleoperation, exoskeletons, etc. There is a strong focus on practical assignments on actual hardware, where the students work with haptic devices to implement the theory through open-ended projects.

• WB1630-20 Statica [2024-present] (Bachelor in Mechanical Engineering, TU Delft)

• AR0122 1:1 Interactive Architecture Prototypes Workshop [2021-present] (Master in Architecture, Urbanism & Building Sciences, TU Delft)

• RO47015 Applied Experimental Methods [2020-2021] (Master in Robotics, TU Delft)

• RO47006 Human-Robot Interaction [2020-present] (Master in Robotics, TU Delft)

• ME41015 Applied Experimental Methods: Human Factors [2019–2020] (Master in Mechanical Engineering, TU Delft)

• ME41070 The Human Controller [2019–2020] (Master in Mechanical Engineering, TU Delft)