LRLearn Robotics

A practical path from first principles to autonomous robot systems.

The goal is not to collect courses. The goal is to build a sequence where math, programming, electronics, controls, sensing, and robot software reinforce each other.

Stage 1. Foundations

Build the math, programming, and physics base needed to reason about robots as moving physical systems.

Learn to describe motion, write small simulations, and explain robot behavior with math instead of guesswork.

CalculusPythonMechanicsLinear algebraRobotics overview

Outcomes

  • Write clean Python programs and simple simulations.
  • Use vectors, matrices, and physics to describe motion.
  • Explain how sensing, planning, and control fit together.

Checkpoints

  • Simulate a point robot moving through 2D space.
  • Plot position, velocity, and acceleration from sampled data.
  • Explain frames, vectors, and basic forces in plain language.

Practice loop

  • Spend most build time in Python notebooks or small scripts.
  • Keep a short engineering log for every mini-experiment.
  • Pair every math topic with a visible robot-motion example.

Related projects

  • Use the previous stage projects as review before moving on.

Stage 2. Core Engineering

Add algorithms, electronics, probability, and dynamic systems so software can meet hardware cleanly.

Connect software decisions to physical signals, circuit limits, and system behavior over time.

Differential equationsData structuresE&MProbabilityCircuits

Outcomes

  • Analyze dynamic behavior and uncertainty.
  • Choose data structures for planning and perception tasks.
  • Prototype and debug basic electronic subsystems.

Checkpoints

  • Breadboard and measure a sensor circuit safely.
  • Use graph search to route through a grid world.
  • Read a datasheet well enough to choose a motor driver or sensor.

Practice loop

  • Use a multimeter before trusting software readings.
  • Write short notes on voltage, current, timing, and failure symptoms.
  • Convert project bugs into repeatable tests or measurements.

Related projects

  • Programmable RC Car

Stage 3. Robotics Core

Study the robot-specific core: kinematics, dynamics, controls, sensors, embedded systems, and ROS 2.

Build the mental model for mobile robots: motion, feedback, sensing, transforms, and robot software architecture.

KinematicsControlsSensorsEmbedded systemsROS 2

Outcomes

  • Model robot motion with transforms and Jacobians.
  • Implement PID and state-space control loops.
  • Build ROS 2 nodes, topics, services, and transforms.

Checkpoints

  • Tune a PID loop and explain the effect of each term.
  • Estimate robot pose from encoder or IMU data.
  • Build a small ROS 2 graph with nodes, topics, and launch files.

Practice loop

  • Log data before tuning control loops.
  • Draw transform trees and message flow diagrams.
  • Separate hardware tests from autonomy tests.

Related projects

  • Autonomous 9-Inch Development Quadcopter

Stage 4. Specialization and Capstone

Turn the core into autonomy with perception, planning, learning, safety, and an integrated capstone.

Integrate perception, planning, and control into complete systems with clear safety and testing habits.

Computer visionMotion planningMachine learningSafetyCapstone

Outcomes

  • Use vision and learning models for robot perception.
  • Plan paths with graph search, sampling, and trajectories.
  • Deliver an integrated robot system with documentation.

Checkpoints

  • Compare at least two planning or perception approaches on the same task.
  • Run a robot behavior in simulation before hardware testing.
  • Publish a capstone writeup with architecture, tests, failures, and demo media.

Practice loop

  • Keep safety constraints visible in every design review.
  • Prefer measurable demos over vague autonomy claims.
  • Document assumptions, limits, and known failure modes.

Related projects

  • Use the previous stage projects as review before moving on.