Xingxin on Bug This week marked my first time delivering a lecture and lab for RoboFab: Robotic Fabrication with Python as a teaching assistant.
Preparing the material for the onsite demo took far longer than I anticipated, and I quickly realized just how difficult it is to create lectures and labs that are truly engaging. My appreciation and respect for Stanford University’s teaching quality have deepened even further. I still remember studying the CS103: Mathematical Foundations of Computing and being impressed, not just by the lecture quality, but especially by how well-designed the exercises were. That level of excellence represents countless hours of hard work by lecturers and TAs.
I now better understand why courses slides and content often remain largely unchanged year after year: it’s genuinely demanding work to develop quality materials. One aspect I am particularly grateful for, though, is the “learning by teaching” effect. Preparing environments for students to experiment with robots onsite forced me to develop a comprehensive understanding of trajectory generation and the specific requirements of Franka Research 3.