Princeton's Introduction to Robotics! 🎓 Princeton University released their full Introduction to Robotics course publicly with lecture videos, notes, slides, and assignments. This course provides fundamental theoretical and algorithmic principles behind robotic systems with hands-on experience. Topics covered: → Feedback Control (dynamics, PD control, Linear Quadratic Regulator) → Motion Planning (discrete planning with BFS/DFS, optimal planning with Dijkstra/A*) → State Estimation, Localization, and Mapping (Bayes filtering, Kalman filtering, particle filtering, SLAM) → Vision and Learning (optical flow, deep learning, convolutional networks, reinforcement learning), and broader topics including robotics and law, ethics, and economics. Assignments include theory, programming, and hardware implementation components. The final project has students program drones for vision-based navigation with attached cameras transmitting real-time images. All lecture videos, notes, slides, and assignments are freely available. Prerequisites include multivariable calculus, linear algebra, basic probability, basic differential equations, and some programming experience in Python. ‼️ GO FOR IT: irom-lab.princeton.edu/intro… ~~ ♻️ Join the weekly robotics newsletter, and never miss any news → ziegler.substack.com