ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Robotics

TL;DR

ARtonomous is a virtual robotics platform that introduces middle school students to reinforcement learning, fostering understanding, engagement, and curiosity about machine learning without the need for physical equipment.

Contribution

The paper presents a low-cost, virtual robotics environment that effectively teaches reinforcement learning concepts to middle school students, addressing accessibility barriers.

Findings

Students gained understanding of reinforcement learning.

High engagement and curiosity levels observed.

Feasibility of virtual robotics for ML education demonstrated.

Abstract

Typical educational robotics approaches rely on imperative programming for robot navigation. However, with the increasing presence of AI in everyday life, these approaches miss an opportunity to introduce machine learning (ML) techniques grounded in an authentic and engaging learning context. Furthermore, the needs for costly specialized equipment and ample physical space are barriers that limit access to robotics experiences for all learners. We propose ARtonomous, a relatively low-cost, virtual alternative to physical, programming-only robotics kits. With ARtonomous, students employ reinforcement learning (RL) alongside code to train and customize virtual autonomous robotic vehicles. Through a study evaluating ARtonomous, we found that middle-school students developed an understanding of RL, reported high levels of engagement, and demonstrated curiosity for learning more about ML. This research demonstrates the feasibility of an approach like ARtonomous for 1) eliminating barriers to robotics education and 2) promoting student learning and interest in RL and ML.