2026 ASEE Annual Conference & Exposition

A Progressive Robotics Curriculum: From Foundations to Machine Learning-Enabled Autonomous Systems

Presented at Electrical and Computer Engineering Division (ECE) Technical Session 8

This paper outlines a three-course robotics sequence developed at [Institution Name Redacted for Review] designed to build student expertise through hands-on learning and carefully scaffolded skill progression. The introductory course focuses on microcontroller programming and hardware interfacing, establishing a foundation in low-level control. The second course transitions students to Python and the Robot Operating System (ROS), applying sensor fusion, navigation, and autonomous behavior in mobile robotics projects. The advanced course shifts toward mathematical rigor, introducing neural networks, Kalman filtering for target tracking, and statistical estimation techniques including maximum likelihood and maximum a posteriori methods, all framed within machine learning theory applied to robotic systems.

Students progress from direct hardware manipulation to autonomous systems and ultimately to theoretical frameworks underpinning robotic intelligence. The curriculum balances practical implementation in early stages with analytical depth in the advanced course. Team-based projects throughout support ABET outcomes while fostering collaboration and system-level thinking. Assessment data show strong gains in technical proficiency and student confidence. We reflect on pacing, hardware choices, and the transition from implementation to theory, offering a model other institutions can adapt to prepare students for careers in autonomous systems and robotics engineering.

Authors

Dr. Stanley Baek United States Air Force Academy [biography]

Dr. Stanley Baek is an Associate Professor in the Department of Electrical and Computer Engineering at the U.S. Air Force Academy (USAFA), which he joined in 2019. His current research interests include intelligent behavioral control of multiple robots, robust intelligence of multiple heterogeneous unmanned vehicles for navigation, searching, and tracking of multiple targets, and intelligent consensus algorithms for multiple mobile robots. He received his Bachelor’s, Master’s, and Ph.D. degrees from the Department of Electrical Engineering and Computer Sciences, University of California, Berkeley. From 2014 to 2019, he was an Assistant Professor at the University of Michigan, where he developed a new undergraduate robotics engineering program. From 2012 to 2014, he was a postdoctoral research engineer at the U.S. Air Force Academy, where he developed multiple-coordinated control algorithms for unmanned aerial vehicles performing a variety of tasks including intelligence, surveillance, reconnaissance, and tactical operations. In 2008, he was an electrical engineer at the U.S. Army Research Lab, where he investigated passive wing rotation of biologically inspired flapping wing robots with compliant joint mechanisms and developed design principles to mimic insect wing kinematics for optimal lift force.

Download paper (563 KB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.

» Download paper

« View session