First Principles Engineering
Stop memorising. Start reasoning. The method used by the world's best engineers to design from scratch.
What is First Principles Thinking?
First principles thinking means breaking a problem down to its most fundamental truths and reasoning up from there — rather than by analogy from what others have done.
Aristotle defined it as "the first basis from which a thing is known." Elon Musk uses it to reinvent industries. In robotics, it's the difference between a builder who follows tutorials and an engineer who creates new systems.
"Reason from first principles rather than by analogy. If you reason by analogy, you're taking existing constraints as given. First principles lets you design around those constraints."
How to Apply First Principles in Robotics
1. Identify the Core Problem
What does the robot actually need to accomplish? Strip away assumptions about how robots "should" look or behave. Define the problem in physics terms: forces, positions, velocities, signals.
2. Break Down the System
Decompose the robot into its fundamental subsystems: power, sensing, actuation, computation, communication. Understand each independently before integrating.
3. Understand the Physics
Every robot behaviour has a physical explanation. Torque, inertia, PWM signals, impedance — understanding these lets you design precisely instead of tuning blindly.
4. Build the Simplest Working Version
Start with the minimum viable system that demonstrates the core principle. This reveals real constraints that assumptions would have hidden.
5. Iterate From Evidence
Every test gives you data. Update your model, not just your code. First principles engineers iterate on their understanding, not just their implementation.
6. Generalise the Solution
Once you understand why it works, abstract the principle. A solution derived from first principles can be applied to new problems — one learned by analogy cannot.
First Principles in Practice: Robotics Examples
| Problem | Analogy Thinking | First Principles Thinking |
|---|---|---|
| Robot won't balance | Increase PID gains | Model the inverted pendulum dynamics; understand the plant transfer function |
| Motor runs hot | Add a heatsink | Calculate I²R losses; redesign duty cycle or choose different motor |
| Sensor reading is noisy | Add a low-pass filter | Identify the noise source; determine if it's electrical, mechanical, or algorithmic |
| Robot veers off path | Tune wheel speeds | Measure wheel diameter variance; implement odometry correction |