Using Math and Logic to Train Robots
Using math to teach robots how to think isnât science fiction. Itâs Brendon Johnsonâs research.
A student in the ±«Óătvâs Master of Science in Computer Engineering program, Johnson spends his days in the Bio-Inspired Robotics Lab under the guidance of Professor Alfredo Weitzenfeld. There, he is doing more than simply programming machines. Heâs teaching them to learn.
âIâve always loved math and logic,â he said. âWith reinforcement learning, you take math and logic and let the machine figure things out over time. It becomes more than just programming. It becomes learning.â
He shares an example of how this works in the lab. In one project, a small robot on wheels edges forward, hesitates at a junction, turns left, bumps into a wall, and tries again. Itâs not following a map or a script. Itâs learning, slowly building knowledge from each success or mistake.
âReinforcement learning mimics how animals or humans learn,â Johnson explained. âSo, if you do something right, you get a reward. If you do something wrong, you get punished. The idea is to train a robot or agent to figure out the best action for each situation.â
So rather than feeding the robot a rigid list of instructions, Johnson designs algorithms that allow it to experiment. The machine earns rewards for behaviors that help it navigate a maze or complete a task and learns to avoid the ones that donât. Over time, it becomes smarter and more capable.
How do you reward a machine?
âUsually itâs a numerical score,â Johnson explained. âThe robot gets a higher value when it performs a desired action, like reaching the goal, and a lower or negative value when it fails. Over time, the machine learns which actions earn better scores.â
A Program That Builds Deep Knowledge and Practical Skills
After earning a bachelorâs degree in computer engineering, Johnson knew that he wanted to specialize in reinforcement learning, a field he described as âkind of niche.â
âI graduated from a great program at Brigham Young University, where they had great engineers and computer scientists, but none of them had conducted research in this area. I learned about the work Dr. Weitzenfeld does in his lab and that was a big part of the reason I chose ±«Óătv,â he said. âI liked the mix of robotics and reinforcement learning which is inspired biology.â
The New Mexico resident said the affordable tuition helped with his decision, too. âI was looking around the country for good schools, discovered the robotics program and research opportunities, and realized ±«Óătv was a good value, too,â he said.
Johnson opted for the thesis track of the MS in Computer Engineering program. It gave him a strong foundation in computing systems, and he had the chance to take varied electives while specializing in machine learning and robotics.
He says the variety of elective courses made the program enjoyable. He is quick to point out, though, that interesting and enjoyable doesnât equate to easy. The courses were rigorous, and the faculty pushed him at times.
âThe courses were challenging but always applicable,â he said. âWhat you learn in class, you can test in the lab. That combination of theory and practice is what makes it stick.â
Faculty support also played a central role. Under Weitzenfeldâs mentorship, Johnson gained the confidence to refine his ideas and take initiative as a researcher.
âI had the freedom to explore and solve problems on my own, but I always knew I could get feedback or guidance,â he said. âThat balance helped me grow.â
Looking Ahead
Johnson defended his thesis last month and will soon start the doctoral program at ±«Óătv. In his paper, âHierarchical Reinforcement Learning in Multi-Goal Spatial Navigation with Autonomous Robots,â he writes about testing manual versus automatic subâgoal creation, examining termination function frequency and demonstrating clear advantages in performance and adaptability. In the doctoral program, he plans to explore how experience-driven learning and computer vision can work together to help machines better interpret their surroundings.
âItâs exciting to build something that learns,â he said. âItâs not just programming a machine. Itâs teaching it how to improve, adapt and solve new problems, and thatâs what the future is about.
Looking ahead, Johnson hopes to apply his research in real-world tech fields, especially autonomous systems. âIâd like to do reinforcement learning in robotics,â he said. Whether in cars, drones, or future smart devices, heâs focused on whatâs next: Building systems that can truly learn.