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

arXiv: https://arxiv.org/abs/2207.08974 · HTML: https://mlsanity.com/paper/2207.08974/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform](https://mlsanity.com/paper/2609.25674/) (2026)

## Abstract (arXiv)

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.