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Evolutionary Robotics

Explore the significance of embodiment in evolutionary robotics, encompassing body morphology, control architecture, and environmental interactions. The challenges of simulation and real-world implementation are examined, alongside proposed solutions for evolving neurocontrollers and morphologies. Learn about evolutionary approaches and relevant literature in this dynamic field.

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Evolutionary Robotics

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  1. Evolutionary Robotics Teresa Pegors

  2. Importance of Embodiment • Embodied system includes: • Body – morphology of system and movement capabilities • Control Architecture – nervous system, normally adaptive and plastic. • Environment – all things external to the system but can include system as well. • All 3 dynamically coupled to each other • Can we synthesize such a system in an evolutionary context?

  3. Simulation vs. Real World • Problems with Simulation: • Not all physical properties are simulated • sensors return perfect values • Same sensors are considered exactly same • Problems with Real World: • Limited resources • Time constraint • Makes doubly difficult to evolve both controllers and morphology

  4. General Solutions (Miglino, Lund, and Nolfi 1996) - evolving neurocontrollers • Look-up Table • Sensor readings are taken from large combination of orientations and distances • Allows for intrinsic differences in sensors • Accounts for idiosyncrasies of environment • After transfer to real world, run a few more generations • Allows system to regain lost fitness

  5. General Solutions (cont’d) • “Conservative Position Noise” • Perception is as if farther or closer than really are, determined by randomly selected axis • Reproduces effects caused by illuminations/shadows/etc. NO NOISE CONSERVATIVE POSITION NOISE

  6. Evolving Morphology (Simulation) • Karl Sims • Recursive, graph based GA • Not physically realistic • Josh Bongard • Physically realistic environment • “Artificial Ontogeny (AO)” • Differential gene expression • Diffused gene products • Modular (spheres)

  7. (Simulation -> Real World) (Jordan Pollack) [1] Universal [3]Efficient [2] Conservative [4]Buildable • Morphology w/o Controller

  8. (Simulation -> Real World) 2) 2D modular system from L-System • Reduction of dimensionality • Re-usable modules lowers complexity

  9. (Simulation -> Real World) 3) Automatic “design and manufacture” of 3D systems • Large difference between physical and virtual environment • Closer to evolving w/o human intervention

  10. Relevant Literature • Nolfi S. and Floreano D. (2000). Evolutionary Robotics. Cambridge: MIT Press. • H. Lipson and J. B. Pollack (2000), "Automatic design and Manufacture of Robotic Lifeforms", Nature 406, pp. 974-978. • Funes, P. and Pollack, J. (1999). “Computer Evolution of Buildable Objects”. In Evolutionary Design by Computers. P. Bentley (editor). Morgan Kaufmann, San Francisco. pp. 387-403. • Bongard, J. C. and R. Pfeifer (2003) Evolving Complete Agents Using Artificial Ontogeny, in Hara, F. and R. Pfeifer, (eds.), Morpho-functional Machines: The New Species (Designing Embodied Intelligence) Springer-Verlag, pp. 237-258. • Sims K. "Evolving Virtual Creatures" Computer Graphics (Siggraph '94 Proceedings), July 1994, pp.15-22.

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