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Resilient Evolutionary Robotics for Manufacturing and Construction

Explore three existing approaches to evolutionary robotics for optimizing robot behavior in manufacturing and construction. Learn about their advantages, challenges, and the alternative Estimation-Exploration Algorithm (EEA) approach.

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Resilient Evolutionary Robotics for Manufacturing and Construction

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  1. Manufacturing versus Construction

  2. Resiliency: adaptation to constant change

  3. Evolutionary robotics approaches Evolve the controller of a robot to automatically discover (near-)optimal behavior Three existing approaches to evolutionary robotics: Evolve controllers directly on a physical robot. Requires 100s or 1000s of physical evaluations. Create a simulation of the robot, and perform some or all of controller evolution in simulation before transferal to the physical device. Requires a human to hand craft the simulator; “Reality gap” problem. Adapt controllers on the physical robot from an original, hand-created controller Requires a human to hand craft the original controller.

  4. Evolutionary robotics approaches Evolve the controller of a robot to automatically discover (near-)optimal behavior Three existing approaches to evolutionary robotics: Evolve controllers directly on a physical robot. Requires 100s or 1000s of physical evaluations. Create a simulation of the robot, and perform some or all of controller evolution in simulation before transferal to the physical device. Requires a human to hand craft the simulator; “Reality gap” problem. Adapt controllers on the physical robot from an original, hand-created controller Requires a human to hand craft the original controller. Alternative approach—The Estimation-Exploration Algorithm (EEA):

  5. Typical experiment

  6. Typical experiment Motor 5 Motor 1

  7. Typical experiment

  8. Evaluating candidate self-models Does not tilt Tilts to the right Does not tilt Higher error Lower error

  9. Typical experiment

  10. Typical experiment

  11. Typical experiment

  12. Typical experiment

  13. Typical experiment

  14. Typical experiment

  15. Typical experiment

  16. Typical experiment

  17. Typical experiment

  18. Typical experiment

  19. The Estimation-Exploration Algorithm (EEA) applied to a single robot Phenotype: Fitness: Estimation Exploration Phenotype: Fitness: Exploitation

  20. Intelligent testing: 13 out of 30 runs prodeuce successful models

  21. Generating behaviors using an optimized model 0.0s 1.3s 1.9s 2.1s 2.3s 2.6s 3.1s 3.7s 4.0s 4.9s 5.2s 4.5s

  22. Mean predictive ability of an optimized model

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