What problem does it solve? Researchers entering the automation and control domain face hundreds of papers spread across SLAM, robust control, adaptive control, MPC, reinforcement learning, and assistive navigation, with no unified map of how these subfields relate. This Skill distills that landscape into a navigable knowledge index so you can locate core papers, understand methodological consensus and divergence, and position your own work. ## Core Features & Use Cases - Six-Domain Knowledge Map: Covers autonomous navigation, blind guidance (BVI), robust control, adaptive control, RL control, and MPC with curated paper lists in references/research/. - Distilled Insights: Provides 7 cross-domain methodological consensuses, 6 school-of-thought divergences with convergence trends, and a 5-layer technology stack model. - Quick-Reference Paper Tables: Entry-point tables organized by research question (e.g., RL+MPC fusion, SLAM genealogy, MPC fundamentals) with venue and year. - Use Case: A PhD student starting a safe RL project asks where their work fits; the Skill points them to the Safe RL survey, the CBF+RL convergence trend, and the constraint-handling consensus shared with MPC. ## Quick Start Ask the assistant to show the core papers and methodological consensus for a chosen control subfield such as robust control or MPC.
