1. Evolving Motor Techniques for Artificial Life Kelley Hecker Period 7

2. Introduction 3D Simulator, breve  OpenGL display  Able to produce physical simulations  steve language  User-created agents

3. Introduction Evolving Creatures  Creatures develop more advanced motor techniques  Progression from random movements to sophisticated patterns  Possibility of specialized creatures

4. Introduction Other Projects  Karl Sims, Evolving Virtual Creatures  Nicolas Lassabe, implementation of Sims’ work

5. Development Procedures  init Defined for every object Called when the object is created For Control objects, sets up the entire simulation. For agents, creates physical object and implements variables  iterate Called at each timestep of the simulation Controls agents’ movements

6. Development Project Progression  Physics tests Drop test Joint test Walk test Swim test  Genetic Algorithm tests Neuron test Evolution test (in progress) ‏

7. Development Physics Tests Creature drop test. Joint test. Red parent with purple child, connected by BallJoint.

8. Development Physics Tests (cont.)‏ Walk test. The creatures joints had a constant velocity. First swim test. In error, only the velocity of the last joint was used, causing the creature to spin. Second swim test. http://www.youtube.com/watch?v=zaQu9OEr7E8

9. Development Genetic Algorithm Tests  Evolution test So far, the simulation goes through ten different random creatures like the one above. It should then choose the best one and pass its velocities on to the next generation, although this is not working yet. Neuron test. The creature moves based on neuron- modified sensor inputs. Each creature has a different random pattern. http://youtube.com/watch?v=WzUvEubEqJI

10. Development Project Testing  Fitness tests Measures the success of a motor method Progression of fitness level shows evolution of technique  Physics and genetic tests See previous slides

11. Development Algorithms  Combination of neuron-generated and gene- generated creatures  Neurons Take sensor (input) values from the joint angles, and modify them Values are passed on as effector (output) values Image © Karl Sims

12. Development Problems  Combining neuron modification with parental genes to allow for random evolution Simply using neurons always produces a random walking motion Does not allow for evolution  Current version of simulation freezes after 10 creatures

13. Conclusion Present Results and Findings  Creatures move about in environment  Simulation does not yet compare fitness levels  Neurons seemingly cannot be used on data passed from the previous generation  Neurons produce random effectors

14. Conclusion Changes in Plan  Moving away from Karl Sims’ ideas  Combination of neuron-guided and gene-guided creatures