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

SBIR第二阶段项目研究如何在虚拟机器人电脑游戏中通过基于游戏的学习让学生参与STEM(科学、技术、工程和数学)教育的问题。在游戏中，学生们设计、组装和训练虚拟行走机器人，与他们的同行机器人竞争，以确保在一次虚拟火星任务中获得一席之地。该项目分析了竞争和合作对学习和保持关键机器人控制概念的影响。该项目将制作一款低成本的教育电脑游戏，比起目前使用机器人硬件的方法，更多的美国初中生和高中生可以通过机器人模拟游戏学习生物力学、数学、物理和计算机算法。该产品为美国教育系统提供了一种解决方案，可以迅速吸引那些原本可能拒绝参加STEM的学生。STEM领域的职业可以带来高薪工作，这反过来又可以提高美国的生产率和经济格局。该项目的教育技术创新是一个基于云的机器人进化游戏引擎，学生们可以作为一个群体来进化行走的两足机器人的行为。机器人行为的进化发生在三个层面上：1)微观尺度，其中一个人或一小群学生进化出单一的机器人行为；2)中尺度，群体交换个人行为，形成一个行为序列，以迎接游戏层面的挑战。(例如，链接一系列行为以站立、行走、跳跃和转弯以清除一段障碍并达到奖励目标)，3)宏观尺度，社区通过重用和采用最有效的行为来演变一组累积的行为，丢弃失败的行为并创造新的游戏级别挑战。特别是，本项目研究了个体和小组学习如何通过调整奖励激励、竞争动机、新奇和惊喜以及挑战水平的难度来影响学习。在比赛中，游戏收集关于学生学习水平和机器人性能的数据，以回答两个具体问题：a)学生通过小组或大小组协作/竞争学到的东西更多，以及b)学生更多地参与到小规模或大规模的众包环境中。

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.