Manufacturing versus Construction

Resiliency: adaptation to constant change

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.

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):

Typical experiment

Motor 5 Motor 1 Typical experiment

Evaluating candidate self-models Does not tiltTilts to the rightDoes not tiltHigher errorLower error

Typical experiment

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

Intelligent testing: 13 out of 30 runs prodeuce successful models

0.0s1.3s 1.9s 2.1s 2.3s2.6s 3.1s3.7s 4.0s 4.5s 4.9s5.2s Generating behaviors using an optimized model

Mean predictive ability of an optimized model