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This paper presents a validated curriculum framework that bridges the gap between complex research workflows in reinforcement learning and humanoid robotics with accessible, low-cost open-source platforms for high school students. By structuring activities around parallel disciplinary tracks and progressive simulation-to-hardware integration, the design effectively addresses key pedagogical tensions such as balancing authenticity with cognitive load. The work establishes a structured pathway for educators to introduce advanced AI-driven robotics concepts to novice learners without requiring extensive prior expertise.
Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform
arXiv:2609.25674v1 Announce Type: new Abstract: Lower cost open source robots and reinforcement learning (RL) simulation tools create new opportunities for precollege students to engage with contemporary robotics. However, translating a complete research workflow, spanning mechanical assembly, electrical setup, simulation, policy learning, system identification, and physical deployment, into a coherent course for novice learners remains challenging. We present an integrated robotics course framework that organizes these activities around a shared robotic artifact. The framework combines parallel disciplinary tracks, sequencing based on technical dependencies, progressive integration of simulation and hardware, layered performance checkpoints, and structures for balancing collaborative work with individual accountability. We illustrate the framework through a high school curriculum organized around a robot project in which pairs of students assemble an open source humanoid robot, train a walking policy in simulation, and deploy it on the physical platform. The framework was developed through an iterative design process that included formative review by five experts in robotics research, engineering, secondary STEM education, and curriculum design. Expert feedback highlighted three central design tensions: authenticity versus cognitive load, system integration versus timely visible progress, and team construction versus individual accountability. These tensions informed the final framework presented in this paper. This work offers a structured approach for adapting robotics research workflows into interdisciplinary precollege courses; future classroom studies are needed to examine implementation and student learning.
Abstract:Lower cost open source robots and reinforcement learning (RL) simulation tools create new opportunities for precollege students to engage with contemporary robotics. However, translating a complete research workflow, spanning mechanical assembly, electrical setup, simulation, policy learning, system identification, and physical deployment, into a coherent course for novice learners remains challenging. We present an integrated robotics course framework that organizes these activities around a shared robotic artifact. The framework combines parallel disciplinary tracks, sequencing based on technical dependencies, progressive integration of simulation and hardware, layered performance checkpoints, and structures for balancing collaborative work with individual accountability. We illustrate the framework through a high school curriculum organized around a robot project in which pairs of students assemble an open source humanoid robot, train a walking policy in simulation, and deploy it on the physical platform. The framework was developed through an iterative design process that included formative review by five experts in robotics research, engineering, secondary STEM education, and curriculum design. Expert feedback highlighted three central design tensions: authenticity versus cognitive load, system integration versus timely visible progress, and team construction versus individual accountability. These tensions informed the final framework presented in this paper. This work offers a structured approach for adapting robotics research workflows into interdisciplinary precollege courses; future classroom studies are needed to examine implementation and student learning.
6 pages, 4 figures, 4 tables. Accepted for presentation at the IEEE International Conference on Teaching, Assessment, and Learning for Engineering (TALE 2026)
Robotics (cs.RO); Artificial Intelligence (cs.AI)
arXiv:2609.25674 [cs.RO]
(or arXiv:2609.25674v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2609.25674 arXiv-issued DOI via DataCite (pending registration)
Full text under the publisher's open licence · 261 words
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