AI & ML
Optimization, Optimal Control, and Reinforcement Learning for Robotics
arXiv cs.RO0 views
arXiv:2610.32538v1 Announce Type: new Abstract: These lecture notes provide an introduction to optimization, optimal control, and reinforcement learning for robotics, emphasizing their mathematical connections and practical relevance. They present established material compiled and edited by Yonghyeon Lee, rather than new research results. The notes were developed through a human and AI collaborative writing process, with AI assistance in drafting, organizing, and refining the exposition. This first version is a preliminary collection of notes, not yet a fully developed textbook, and currently contains no figures or graphs. The notes may be revised, corrected, and expanded in future versions.
Source:https://arxiv.org/abs/2610.32538PreviousFacultative Swarming: A Capability, Not a Category, for Multi-Robot SystemsNextUAV-SwarmAgent: Scalable Multi-UAV Reconnaissance and Task Allocation via Code Generation and Vision-Language Reasoning
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