Using Educational Robotics to Motivate Complete AI Solutions

Robotics is a remarkable domain that may be successfully employed in the classroom both to motivate students to tackle hard AI topics and to provide students experience applying AI representations and algorithms to real-world problems. We show how the robot obstacle-detection problem can motivate learning neural networks and Bayesian networks. We also show how the robot-localization problem can motivate learning how to build complete solutions based on particle filtering. We believe that expanding handson active learning to additional AI classrooms provides value both to the students and to the future of the field itself.