1. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 1 Real Artificial Life: Robots

2. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 2 Biorobotics Question: How can the study of robots help us better understand biological organisms? Biorobotics: close collaboration of biologists and roboticists mostly trying to understand the principles of various forms of locomotion Biology can ask: “how does phenomenon X work in species Y?” “analytic approach” E.g.: How are which parts of the cockroach nervous system controlling its walking behavior? Experimental limitations: can destroy parts of the system but not add or replace parts. Can not ask: “does it have to be this way?”, “could it work any other way?” Robotics can ask: “what classes of control mechanism are suited for generating walking behaviors for what leg configurations in what environments?” “synthetic approach”

3. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 3 Robotic Insects roboinsects: six-legged or eight-legged walking robots have given important clues to understanding the neural control mechanisms in walking insects

4. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 4 Robotic Lobster Joseph Ayers: from biologist to roboticist

5. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 5 Robotic Fish MIT “robopike”MIT “robotuna”

6. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 6 Robotic models of fish allow systematic study of principles governing efficient swimming.

7. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 7

8. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 8 Robotic Brachiation Robotic models of brachiation used as test arena to study neural models of learning of motor control Other model systems include: snakes, butterflies, birds, …

9. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 9 Robotics & Cognitive Science (why you should care about robots) Building artificial brains: helps us better understand the problems that the brain is solving allows to test cognitive theories forces them to be specific allows to ask more general questions When do we “understand” the brain? When we can rebuild it!? Studying real brains: since brain produces intelligent behavior, robotics researches can learn from better understanding of brain function. Biological research inspiring new architectures/algorithms

10. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 10 Note: the previous argument highlighted the benefits of computational modeling. But why robots? Embodiment

11. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 11 Embodiment Therefore, building artificial brains (neural network models) is not enough but we have to give these artificial brains bodies and environments, too: “Cognitive Robotics” Previous argument did not really talk about robots per se, but just the benefits of computational modeling in general. So why robots? Brains are not isolated entities but are in continuous interaction with their environment mediated through their bodies and senses. For a complete understanding of cognition we need to study this interaction of brains with their natural, social, and cultural environment.

12. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 12 The classic AI approach world modelsensorsactuators old AI approach to intelligent behavior Problems: symbol grounding: how can you extract the symbols from the environment? brittleness: systems fail in even slightly different domains efficiency: creating and maintaining a comprehensive world model is expensive Explain intelligence in terms of symbolic knowledge, logic, search, exact reasoning: “IF hungry THEN get food” discrete symbols for the state of the robot and the world planning is analogous to finding a mathematical proof perception/ modeling planning/ execution

13. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 13 W.G. Walter: Machina Speculatrix (~1950) Elsie

14. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 14 Lesson learned: complex, interesting, somewhat unpredictable behavior can emerge when these simple robots interact with their environment and each other.

15. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 15 modern implementation with LEGO Mindstorm robots

16. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 16 V. Braitenberg: “Vehicles” (1984)

17. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 17

18. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 18 Braitenberg's primary conclusion in Vehicles is the law of uphill analysis and downhill synthesis (invention). The vehicles he describes can produce very complicated behavior, yet the underlying mechanisms are very simple. When we try to deduce the mechanisms controlling these creatures by analyzing their behavior, we overestimate their complexity. Conversely, we can invent "downhill," creating creatures that can exhibit complicated behavior but are composed of only simple parts. after Charlie Coglianese, Kushal Dave, and Eric Kennedy

19. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 19 This vehicle appears to like cold areas and dislike heat.

20. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 20 Quiz: who shows “fear”, who shows “anger”? motorized wheel light sensor ++ + +

21. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 21 R. Brooks: “subsumption architecture”

22. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 22 Example: layers of behaviors locomotion/obstacle avoidance/exploration/map formation/… “thousands of artificial cockroaches have been built like this” AI focus: manipulating symbols new focus: direct interaction with environment, “subsumption” “intelligence without reason”

23. Jochen Triesch, UC San Diego, http://cogsci.ucsd.edu/~triesch 23 Ghengis Kismet Problem: this approach does not seem to scale well to designing human level intelligence. More modern approaches try to emphasize autonomous learning. The robot undergoes a developmental period of learning like a child.