BreedBot. A software/hardware environment for human breeders of robots
In classical Evolutionary Robotics (ER), robots are evaluated in terms of their ability to perform a task defined by the researcher. Measurements are based on a so-called fitness function. The robot’s probability of producing offspring is proportional to the fitness measured by this function. Consequently the design of the function plays a crucial role in the success of ER experiments (Nolfi e Floreano, 2000). The problem facing ER designers is similar to a classical problem in behaviorist psychology: how to measure the efficiency of training procedures for experimental animals. The supporters of the so-called molecular approach sought to stabilize micro-behaviors which contributed to the animal’s overall performance (Guthrie, 1935, and to some extent Pavlov, 1927). By contrast, the molar approach (Tolman, 1922) preferred to reinforce macro-behaviors leading to a satisfactory end result (e.g. finding a particular target zone, finding a way out of a maze). Experience shows that pragmatically speaking, the two approaches are complementary. Trainers have to consider the peculiarities of particular tasks and particular species of experimental animal and choose the right mix of molecular and molar techniques on this basis. This is exactly the task facing researchers in ER trying to define a fitness function. When Floreano and Mondada want to teach a robot obstacle avoidance they use a ‘molecular’ fitness function1. But when they want their robot to find a target zone they are no longer interested in its individual actions. In this case they use a ‘molar’ function. (Miglino and Lund, 2001). With harder tasks they have to integrate the two approaches (Nolfi, 1997). Outside the lab, trainers, breeders and teachers do not usually follow rigid procedures. When they reward their charges, or when they select them for breeding, they use many different criteria. Sometimes they use measurements, but often they base their judgments on qualitative, contextdependent features which are hard to capture in a mathematical formula. Typically they consider several indicators speed, error rates, exam marks and then decide heuristically which they are going to reward. Could we adopt a similar approach in ER? More specifically, is there some way we could integrate algorithmically-defined fitness functions with the heuristically-based decisions of a human trainer? To investigate this possibility we developed Breedbot2 (Miglino and Gigliotta, 2004), an integrated hardware/software system which allows human ‘breeders’ to breed a small population of robots. We then put Breedbot in the hands of a small group of users, and studied the way they used the system.

Breedbot needs Windows XP, 64 MB ram, 10 MB hard disk space and 1024x768 video resolution .
Actually Breedbots works only at that video resolution.
Visit Breedbot Tutorial
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