Using Explainable AI and Hierarchical Planning for Outreach with Robots

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.