Understanding how robots plan and execute tasks is crucial in today's world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challenging. This work presents an open-source platform, \nameAbbr{}, that simplifies the process using a visual interface that abstracts the details of various planning processes that robots use for performing complex mobile manipulation tasks. Using principles developed in the field of explainable AI, this intuitive platform enables students to use a high-level intuitive instruction set to perform complex tasks, visualize them on an in-built simulator, and to obtain helpful hints and natural language explanations for errors. Finally, \nameAbbr{}, includes an adaptive curriculum generation method that provides students with customized learning ramps. This platform's efficacy was tested through a user study with university students who had little to no computer science background. Our results show that \nameAbbr{} is highly effective in increasing student engagement, teaching robotics programming, and decreasing the time need to solve tasks as compared to baselines.

翻译：在机器人日益融入日常生活的当今世界，理解其如何规划与执行任务至关重要。然而，向非专业人士（如K-12阶段学生）传授机器人规划的复杂性具有挑战性。本研究提出一个开源平台\nameAbbr{}，该平台通过可视化界面简化这一过程，抽象化机器人执行复杂移动操作任务时采用的各种规划流程细节。基于可解释人工智能领域发展的原理，该直观平台使学生能够使用高层级直观指令集执行复杂任务，在内置模拟器上进行可视化，并获得针对操作错误的实用提示与自然语言解释。此外，\nameAbbr{}包含自适应课程生成方法，可为学生提供定制化的学习进阶路径。通过针对计算机科学背景薄弱的大学生开展用户研究，验证了该平台的有效性。实验结果表明，与基线方法相比，\nameAbbr{}在提升学生参与度、教授机器人编程以及缩短任务解决时间方面具有显著成效。