1. Build Intelligence from the bottom up!
Behavioral Robotics
Build Intelligence from the bottom up!

2. AI Debate #3 Is representation needed?
“When we examine very simple intelligence we find that explicit representations and models of the world get in the way.”
“Representation is the wrong unit of abstraction in building the bulkiest parts of intelligent systems.”
Rodney A. Brooks in Intelligence without representation

3. Evolutionary Timeline
Single cell entities arose in the soup 3.5 billion years ago
Fish and vertebrates arose 550 million years ago
Insects – 350 million years ago
Reptiles – 370 million
Primates – 120 million
Man 2.5 million
Agriculture 10,000 years
Writing – 5000 years
Expert knowledge in the last few hundred years

4. Traditional Robotic Architecture
Perception
Modeling
Planning
Task
Execution
Motor
Control
Actuators
Sensors

5. Effects of Traditional Architecture
Single point of failure
Spends a lot of time planning!
Sensor Fusion
Planning is a way of avoiding deciding what to do next!

6. Subsumption Architecture
Reason
Plan Changes
Monitor Changes
Actuators
Sensors
Build Maps
Explore
Wander
Avoid Objects

7. Assumptions
Complex behaviors need not come from a complex control scheme.
Things should be simple!
Build cheap robots in natural spaces.
The world is 3D!
Absolute coordinates don’t work!
Useful worlds are not polyhedra.
Sensors and actuators fail.
Build artificial beings!

8. Subsumption Module
inhibition
FSM
Outputs
Inputs
Reset
Suppression

9. Simple Level 0 Feel Force runaway Turn Sonar Forward Collide force
heading
heading
Sonar
Forward
Collide
encoders

10. Add level 1 Wander Avoid Sonar Feel Force runaway Turn Collide Forward
heading
encoders

11. Mapping / Navigation Since we can’t use: How can we map the world?
absolute coordinates
Sensor fusion
How can we map the world?
Distributed Maps
Maja J. Mataric and R. Brooks

12. Landmark detector
Sonar
compass

14. Kismet

17. Ants

18. Genghis

19. Creature requirements
Cope in a timely fashion in a dynamic environment
Robust w.r.t it’s environment
Maintain multiple goals
Do something!

20. What this approach isn’t!
Not neural nets / connectionism
Use FSM and low connectivity
Not production rules
Not a blackboard
Not German Philosophy
Heidegger has similar ideas, but this is engineering!

21. Limits to Growth How many layers can be stacked up?
How much complexity can happen without central representation
Can learning occur in fixed networks?