Robot Control Learning Map
A growing map for organizing robot control fundamentals, modeling, simulation, and learning-based extensions.
This note collects a learning path for robot control. It is meant to grow as concepts become clearer.
Foundation
- Kinematics
- Dynamics
- Feedback control
- State-space models
- Simulation basics
Questions to Track
- How do modeling assumptions affect control design?
- Which control ideas are most useful before studying learning-based robot policies?
- What examples are simple enough to reproduce and document?
Next Notes to Add
- Basic manipulator kinematics
- PID and state feedback examples
- Simulation setup notes