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Biologically-inspired balancing of inherited and learned behaviors in mobile robots

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Speaker:

Frank Grasso

Frank W. Grasso 1,2,3
1 BioMimetic and Cognitive Robotics Lab, Brooklyn College, The City University of
New York, Brooklyn, NY
2 Ecology, Evolution and Behavior Program, The Graduate Center, CUNY
3 Cognition, Brain and Behavior Program, Psychology, The Graduate Center, CUNY

In experimental ethology we often seek or contrive situations in which two or more
behaviors are placed into conflict. This approach often yields valuable insights into
the central organization of behavioral mechanisms. I will outline research from my lab
exploring the resolution of behavioral conflict within mobile robots as a model for a
development of behavioral conflict resolution in within a single, simple, animal. The
control (decision) systems are based on biological principles rather than technological
applications and are not optimized in an a-priori engineering sense. Rather, they are
endowed with innate preferences that bias their ontogeny. These controllers adapt
continuously within the dual constraints set by inherited behaviors and demands of a
changing environment to reach a developmental equilibrium. These controllers use
recurrent neural networks and implement a form of short-term memory that is inherited
but not experience specific. They are simple compared to animal systems and therefore
more easily analyzed. Yet they are complex compared to many mobile robot controllers
developed for commercial applications or BioRobot implementations used to describe
animal behavior. Our studies provide insights into the causes of variable responses of
an animal presented with the same environmental situation on multiple occasions and
provide explanations of behavioral variability as a result of competing dynamic processes
rather than learning processes, environmental or internal noise.

Video:

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