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Tutorial.floreano

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  • 1. A TUTORIAL Stefano Nolfi Neural Systems & Artificial Life National Research Council Roma, Italy nolfi@ip.rm.cnr.it Dario Floreano Microengineering Dept. Swiss Federal Institute of Technology Lausanne, Switzerland dario.floreano@epfl.ch
  • 2. TUTORIAL Stefano Nolfi & Dario The method fitness function ivV 11 t e s t t e s t t e s t G e n . 0 s e le c t r e p r o d u c e a n d m u t a t e G e n . 1 s e le c t G e n . n … … … . . r e p r o d u c e a n d m u t a t e genotype-to-phenotype mapping
  • 3. TUTORIAL Stefano Nolfi & Dario Behavior-Based Robotics & ER locomote avoid hitting things explore manipulate the world build maps sensors actuators behavior-based robotics [Brooks, 1986] evolutionary robotics ? ? ? ? ? sensors actuators
  • 4. TUTORIAL Stefano Nolfi & Dario Learning Robotics & ER sensors motors desired output or teaching signal [Kodjabachian & Meyer, 1999]
  • 5. TUTORIAL Stefano Nolfi & Dario Artificial Life & ER [Menczer and Belew, 1997] [Floreano and Mondada 1994]
  • 6. TUTORIAL Stefano Nolfi & Dario How to Evolve Robots evolution on the real world [Floreano and Nolfi, 1998] evolution on simulation + test on the real robot [Nolfi, Floreano, Miglino, Mondada 1994]
  • 7. TUTORIAL Stefano Nolfi & Dario Evolution in the Real World mechanical robustness energy supply analysis [© K-Team SA] [© K-Team SA] [© K-Team SA] [Floreano and Mondada, 1994]
  • 8. TUTORIAL Stefano Nolfi & Dario Evolution in Simulation Different physical sensors and actuators may perform differently because of slight differences in their electronics or mechanics. Physical sensors deliver uncertain values and commands to actuators have uncertain effects. The body of the robot and the environment should be accurately reproduced in the simulation. 0 20 320 20 80 0 256 512 768 1024 y z x 0 20 140 200 260 0 256 512 768 1024 y z x 4th IF sensor 8th IF sensor [Nolfi, Floreano, Miglino and Mondada 1994; Miglino, Lund, Nolfi, 1995]
  • 9. TUTORIAL Stefano Nolfi & Dario Designing the Fitness Function FEE functions that describe how the controller should work (functional), rate the system on the basis of several variables and constraints (explicit), and employ precise external measuring devices (external) are appropriate to optimize a set of parameters for complex but well defined control problem in a well-controlled environment. BII functions that rate only the behavioral outcome of an evolutionary controller (behavioral), rely on few variables and constraints (implicit) that be computed on-board (internal) are suitable for developing adaptive robots capable of autonomous operation in partially unknown and unpredictable environments without human intervention. [Floreano et al, 2000]
  • 10. TUTORIAL Stefano Nolfi & Dario Genetic Encoding Evolvability Expressive power Compactess Simplicity [Gruau, 1994, Nolfi and Floreano 2000]
  • 11. TUTORIAL Stefano Nolfi & Dario Adaptation is more Powerful than Decomposition and Integration The main strategy followed to develop mobile robots has been that of Divide and Conquer: 1) divide the problem into a list of hopefully simpler sub-problems 2) build a set of modules or layers able to solve each sub-problem 3) integrate the modules so to solve the whole problem Unfortunately, it is not clear how a desired behavior should be broken down
  • 12. TUTORIAL Stefano Nolfi & Dario Proximal and Distal Descriptions of Behaviors motor space sensory space environment discriminate approach avoid explore distal description proximal description [Nolfi, 1997]
  • 13. TUTORIAL Stefano Nolfi & Dario Discrimination Task (1) explore avoid approach discriminate sensors actuators decomposition and integration walls and cylinders small and large cylinders [Nolfi, 1996,1999]
  • 14. TUTORIAL Stefano Nolfi & Dario Discrimination Task (2) g e n o t y p e p h e n o t y p e explore avoid approach discriminate sensors actuators [Nolfi, 1996]
  • 15. TUTORIAL Stefano Nolfi & Dario Discrimination Task (3) [Scheier, Pfeifer, and Kuniyoshi, 1998] Evolved robots act so to select sensory patterns that are easy to discriminate
  • 16. TUTORIAL Stefano Nolfi & Dario The Importance of Self-organization Operating a decomposition at the level of the distal description of behavior does not necessarily simplify the challenge By allowing individuals to self-organise, artificial evolution tends to find simple solutions that exploit the interaction between the robot and the environment and between the different internal mechanism of the control system. [Nolfi, 1996,1997]
  • 17. TUTORIAL Stefano Nolfi & Dario Modularity and Behaviors Garbage Collecting Behavior Human Design Explore Discriminate Displace in front Pick-up Release C O O R D I N A T E Garbage Collecting Behavior Adaptation ? ? ? ……. ? ? Is modularity useful in ER ? What is the relation between self-organized neural modules and behaviors ? [Nolfi, 1997]
  • 18. TUTORIAL Stefano Nolfi & Dario The Garbage Collecting Task (1) Selector Neurons Output Neurons IR-Sensors LB-Sensor left m. right m. pick-up release m o t o r s ( b )m o t o r s ( a )m o t o r s m o t o r s I R - s e n s o r s & B L - s e n s o r I R - s e n s o r s & B L - s e n s o r I R - s e n s o r s & B L - s e n s o r I R - s e n s o r s & B L - s e n s o r A B C D [Nolfi, 1997]
  • 19. TUTORIAL Stefano Nolfi & Dario The Garbage Collecting Task (2) There is not a correspondence between self-organized neural modules and sub-behaviors Modular neural controller able to self-organize outperform other architectures 0 3 6 9 1 2 0 2 5 0 5 0 0 7 5 0 1 0 0 0 g e n e r a t io n s successfulepochs [Nolfi, 1997]
  • 20. TUTORIAL Stefano Nolfi & Dario Evolving “complex” behaviors Bootstrap problem: selecting individuals directly for their ability to solve a task only works for simple tasks Incremental Evolution: starting with a simplified version of the task and then progressively increasing complexity Including in the selection criterion also a reward for sub-components of the desired behavior Start with a simplified version of the task and then progressively increase its complexity by modifying the selection criterion
  • 21. TUTORIAL Stefano Nolfi & Dario Visually-Guided Robots [Cliff et al. 1993; Harvey et al. 1994]
  • 22. TUTORIAL Stefano Nolfi & Dario Learning & Evolution: Interactions • Different time scales, different mechanisms, similar effects • Learning Advantages in Evolution [Nolfi & Floreano, 1999]: – Adapt to changes that occur faster than a generation – Extract information that might channel the course of evolution – Help and guide evolution – Reduce genetic complexity and increase population diversity • Learning Costs in Evolution [Mayley, 1997]: – Delay in the ability to achieve fit behaviors – Increased unreliability (learning wrong things) – Physical damages, energy waste, tutoring • Baldwin effect [Baldwin, 1896; Morgan, 1896; Waddington, 1942]
  • 23. TUTORIAL Stefano Nolfi & Dario Hinton & Nowlan model [1987] • Learning samples space in the surrounding of the individual • Fitness landscape is smoothed and evolution becomes faster • Baldwin effect (assimilation of features normally « learnt ») • Model constraints: – Learning task and evolutionary task are the same – Learning is a random process – Environment is static – Genotype and Phenotype space are correlated 00?11???0111?0?1?0?1 1 1 ? 0 ? 0 Fitness=correct combination of weights
  • 24. TUTORIAL Stefano Nolfi & Dario Different Tasks [Nolfi, Elman, Parisi, 1994] - Evolving for food - Learning predictions - Learning mechanism=BP - Increased speed & fitness - Genetic assimilation
  • 25. TUTORIAL Stefano Nolfi & Dario Perspectives on Landscape Correlated landscapes [Parisi & Nolfi, 1996] Relearning effects to compensate mutations [Harvey, 1997] (it may hold only in few cases) A C B1 Q P B2 A=weights evolved for food finding C=weights trained for prediction B1, B2= new position after mutation Fitness=higher when closer to A
  • 26. TUTORIAL Stefano Nolfi & Dario Evolutionary Reinforcement Learning • Evolving both action and evaluation connection strengths [Ackley & Littman, 1991] • Action module modifies weights during lifetime using CRBP • ERL better better performance than E alone or RL alone • Baldwin effect • Method validated on mobile robots [Medeen, 1996]
  • 27. TUTORIAL Stefano Nolfi & Dario Evolutionary Auto-teaching • All weights genetically encoded, but one half teaches the other half using Delta rule [Nolfi & Parisi, 1991] • Individuals can live in one of two environments, randomly determined at birth • Learning individuals adapt strategy to environment and display higher fitness Learning No learning
  • 28. TUTORIAL Stefano Nolfi & Dario Evolution of Learning Mechanisms (1) • Encoding learning rules, NOT learning weights [Floreano & Mondada, 1994] • Weights always initialized to random values • Different weights can use different rules within same network • Adaptive method can be applied to node encoding (short genotypes) 1 synapse synapse sign synapse strength Genetically-determined 1 synapse synapse sign learning rule - hebb - postsynaptic - presynaptic - covariance learning rate Adaptive
  • 29. TUTORIAL Stefano Nolfi & Dario Sequential task & unpredictable change • Faster and better results [Floreano & Urzelai, 2000] • Automatic decomposition of sequential task • Synapses continuously change • Evolved robots adapt online to upredictable change [Urzelai & Floreano, 2000]: – Illumination – From simulations to robots – Environmental layout – Different robotic platform – Lesions to motor gears [Eggenberge et al., 1999] Genetically-determined Adaptive
  • 30. TUTORIAL Stefano Nolfi & Dario Summary • Learning is very useful for robotic evolution: – accelerates and boosts evolutionary performance – can cope with fast changing environments – can adapt to unpredictable sources of change • Lamarck evolution (inherit learned properties) may provide short- term gains [Lund, 1999], but it does not display all the advantages listed above [Sasaki & Tokoro, 1997, 1999] • Distinction between learning and adaptation [Floreano & Urzelai, 2000]: – Adaptation does not necessary develops and capitalize upon new skills and knowledge – Learning is an incremental process whereby new skills and knowledge are gradually acquired and integrated
  • 31. TUTORIAL Stefano Nolfi & Dario Competitive Co-evolution • Fitness of each population depends on fitness of opponent population. Examples: – Predator-prey – Host-parasite • It may increase adaptive power by producing an evolutionary arms race [Dawkins & Krebs, 1979] • More complex solutions may incrementally emerge as each population tries to win over the opponent • It may be a solution to the boostrap problem • Fitness function plays a less important role • Continuously changing fitness landscape may help to prevent stagnation in local minima [Hillis, 1990]
  • 32. TUTORIAL Stefano Nolfi & Dario Co-evolutionary Pitfalls The same set of solutions may be discovered over and over again. This cycling behavior may end up in very simple solutions. Solution: Retain best individuals of last few gens (Hall-of-Fame->all gens). Whereas in conventional evolution the fitness landscape is static and fitness is a monotonic function of progress, in competitive co-evolution the fitness landscape can be modified by the competitor and fitness function is no longer an indicator of progress. Solution: Master Fitness (after evolution test each best against all best), CIAO graphs (test each best against all previous best).
  • 33. TUTORIAL Stefano Nolfi & Dario Examples of Co-evolutionary Agents Simulated predator-prey [Cliff & Miller, 1997] Distance-based fitness 100s generations CIAO method et al. Evolution of sensors Ball-catching agents [Sims, 1994] Distance-based fitness Rare good results
  • 34. TUTORIAL Stefano Nolfi & Dario Co-evolutionary Robots • Energetically autonomous • Predator-prey scenarion • Time-based fitness • Controllers downloaded to increase reaction speed • Retain last best 5 controllers for testing individuals • Predators=vision+proximity • Prey=proximity+faster • Predator genotype longer • Prey has initial position advantage Floreano, Nolfi, & Mondada, 1998
  • 35. TUTORIAL Stefano Nolfi & Dario Co-evolutionary Results 20 40 60 80 100 0.2 0.4 0.6 0.8 1 20 40 60 80 100 0.2 0.4 0.6 0.8 1 generations generations best predator fitness best prey fitness d t t d Predators do not attempt to minimize distance Prey maximize distance progress best fun
  • 36. TUTORIAL Stefano Nolfi & Dario Increasing Environmental Complexity 36240 …prevents premature cycling [Nolfi & Floreano, 1999]
  • 37. TUTORIAL Stefano Nolfi & Dario Summary • Competitive co-evolution is challenging because: – Fitness landscape is continuously changing – Hard to monitor progress online – Cycling local minima • When environment is sufficiently complex, or Hall-of-Fame method is used, the system develops increasing more complex solutions • It can work and capitalize on very implicit, internal, and behavioral fitness functions by exploring a large range of behaviors triggered by opponents • When co-evolving adaptive mechanisms, prey resort to random actions whereas predators adapt online to the prey strategy and report better performance [Floreano & Nolfi, 1997]
  • 38. TUTORIAL Stefano Nolfi & Dario Evolvable Hardware • Evolution of electronic circuits http://www.cogs.susx.ac.uk/users/adrianth/EHW_groups.html • Evolution of body morphologies (including sensors) • Why evolve hardware? – Hardware choice constrains environmental interactions and the course of evolution – Evolved solutions can be more efficient than those designed by humans – Develope new adaptive materials with self-configuration and self-repair abilities
  • 39. TUTORIAL Stefano Nolfi & Dario Evolutionary Control Circuits • Thompson’s unconstrained evolution • Xilinx, family 6000, overwrite global synchronization • Tone reproduction • Robot control • Fitness landscape studies (very rugged, neutral networks) Evolvable Hardware Module for Khepera http://www.aai.ca
  • 40. TUTORIAL Stefano Nolfi & Dario Evolutionary Control Circuits • Keymeulen: evolution of vision based controllers • Find ball while avoiding obstacles • Constrained evolution, entirely on physical robot • De Garis: CAM Brain, composed of tens of Xilinx FPGAs, 6000 family • Growth of neural circuits using CA with evolved rules • Willing to evolve brain for kitten robot. Pitfall: speed limited by sensory- motor loop.
  • 41. TUTORIAL Stefano Nolfi & Dario Evolutionary Morphologies • Evolution of Lego Structures [Funes et al,, 1997] • Bridges • Cranes • Extended to objects and robot bodies • see www.demo.cs.brandeis.edu • Example of evolved crane [Funes et al,, 1997]
  • 42. TUTORIAL Stefano Nolfi & Dario Co-evolutionary Morphologies Karl Sims, 1994 Komosinski & Ulatowski, 1999 http://www.frams.poznan.pl Effect of doubling sensor range on body/wheel size [Lund et al., 1997]
  • 43. TUTORIAL Stefano Nolfi & Dario Suggestions for Further Research • Encoding and mapping of control systems • Exploration of alternative building blocks • Integration of growth, learning, and maturation • Incremental and open-ended evolution • Morphology and sensory co-evolution • Application to large-scale circuits • User-directed evolution • Comparison with other adaptive techniques • Further readings: – Nolfi, S. & Floreano, D. Evolutionary Robotics. The Biology, Technoloy, and Intelligence of Self-Organizing Machines. MIT Press, October 2000 – Husbands, P. & Meyer, J-A. (Eds.) Evolutionary Robotics. Proceedings of the 1st European Workshop, Springer Verlag, 1998 – Gomi, T. (Ed.) Evolutionary Robotics. Volume series: I (1997), II (1998), III (2000), AAI Books.
  • 44. TUTORIAL Stefano Nolfi & Dario Evorobot Simulator Sources, binaries, and documentation files freely available at: http://gral.ip.rm.cnr.it/evorobot/simulator.html [Nolfi, 2000]