React to Change
State machines, behavior trees, and decision-making under uncertainty.
What You'll Learn
- Design a finite state machine for a simple robot task
- Build a behavior tree with sequences, selectors, and conditions
- Compare state machines vs behavior trees and choose the right one
- Handle errors and recovery in autonomous robot behavior
- Implement reactive decision-making that adapts to sensor changes
Why Robots Need Decisions
Understanding reactive vs deliberative behavior and why robots need structured decision-making frameworks to handle the real world.
State Machines
Finite state machines are the foundation of robot decision-making. Learn states, transitions, guards, and hierarchical FSMs.
Behavior Trees
Behavior trees are a modular, composable alternative to state machines. Learn sequences, selectors, conditions, actions, decorators, and the tick mechanism.
When to Use What
State machines vs behavior trees — understanding the trade-offs, complexity, reusability, and when to use each (or both).
Error Handling and Recovery
What happens when things go wrong? Learn about error states, watchdogs, fallback behaviors, and graceful degradation.