Academic Explainer

Read the input carefully. Extract the key technical concepts the reader must understand. Organize them into a dependency graph (what must be understood before what). Typical categories:

• Mathematical foundations — linear algebra, probability, calculus of variations, functional analysis, optimization
• Control theory — stochastic optimal control, MPC, Lyapunov stability, Hamilton-Jacobi-Bellman
• Probabilistic methods — Gaussian processes, Bayesian inference, information theory, particle filters
• Robotics-specific — rigid body dynamics, SE(3)/SO(3) geometry, sensor models, planning algorithms
• ML/learning — neural networks, policy gradient, diffusion models, transformers (if relevant)

Step 2 — Assess assumed vs. explained

For each concept, judge:

• Assumed — standard knowledge the paper builds on without explanation
• Explained — the paper introduces or extends this (less need for prior understanding)
• Key to contribution — must be deeply understood to evaluate the paper's novelty

Focus explanations on assumed and key-to-contribution concepts.

Step 3 — Write one note per concept in 01_Phd/topics/

Create one self-contained Obsidian note per concept in 01_Phd/topics/ using mcp__obsidian__write_note.

Before writing, check mcp__obsidian__list_notes on 01_Phd/topics/ — update existing notes rather than overwriting blindly.

Frontmatter (required)

Every topic note must open with YAML frontmatter:

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