Teaching Reinforcement Learning and Humanoid Robotics to High-School Students: An Expert-Validated Curriculum Design on a Low-Cost Open Platform
On the map: Deep reinforcement learning methods → RL for robot locomotion and manipulation
Abstract (arXiv)
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.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for robot locomotion and manipulation, one of 15 directions in Deep reinforcement learning methods. Builds on 6 of its 21 references that are in the map. First posted on arXiv 22 Sep 2026.
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6 of 21 references.
References · 6
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,498
- 2017Sim-to-Real Transfer of Robotic Control with Dynamics Randomizationcited by 672
- 2025ToddlerBot: Open-Source ML-Compatible Humanoid Platform for Loco-Manipulationcited by 9
- 2025Demonstrating Berkeley Humanoid Lite: An Open-source, Accessible, and Customizable 3D-printed Humanoid Robotcited by 2
- 2022ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Roboticscited by 1
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- 2024Learn to Teach: Sample-Efficient Privileged Learning for Humanoid Locomotion over Diverse Terrains
- 2024CTS: Concurrent Teacher-Student Reinforcement Learning for Legged Locomotion
- 2025Robot Trains Robot: Automatic Real-World Policy Adaptation and Learning for Humanoids
- 2026AGILE: A Comprehensive Workflow for Humanoid Loco-Manipulation Learning
- 2026The Open Ant: A Robot Platform for Reinforcement Learning Research
- 2024Grounded Curriculum Learning
- 2024BricksRL: A Platform for Democratizing Robotics and Reinforcement Learning Research and Education with LEGO