SBIR Phase II: An Interactive 3D Game of Evolutionary Robotics for STEM-based Education

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This SBIR Phase II project studies the problem of how to engage students in STEM (Science Technology Engineering and Mathematics) education through game-based learning in a Virtual Robotics computer game. In the game, students design, assemble and train virtual walking robots to compete against their peer robots to secure a seat on a virtual mission to Mars. The project analyses the effects of competition and collaboration on the learning and retention of key robotic control concepts. The project will produce a low-cost educational computer game, where far more U.S. middle schools and high schools students can learn about biomechanics, math, physics and computer algorithms through a robotic simulation game than is possible with current robot hardware approaches. The product provides the American educational system a solution to quickly engage students in STEM who might otherwise turn away. STEM field professions can lead to high paying jobs, which in turn can improve productivity and the economic landscape for America. The project's education technology innovation is a cloud-based robot evolution game engine where students acting as a crowd can evolve the behavior of walking bi-pedal robots. The evolution of robot behavior happens on three levels: 1) the micro scale where an individual or small group of students evolves singular robot behaviors, 2) the mid-scale where groups exchange individual behaviors to form a behavior sequence to meet game level challenges. (For example, link a sequence of behaviors to stand, walk, jump, and turn to clear a section of obstacles and reach the prize goal), 3) the macro scale where the community evolves a cumulative set of behaviors through re-use and adoption of most efficient behaviors, discarding failed behaviors and creating new game level challenges. In particular, this project studies how individual and group learning is affected by adjusting the reward incentive, competitive motivation, novelty and surprise, and challenge level difficulty. During competition, the game gathers data regarding level of student learning and robot performance to answer two specific questions: a) do students learn more through small group or large group collaboration/competition, and b) are the students more engaged in small or large scale crowd-sourced environments.