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Evolutionary robotics: a new scientific tool for studying cognition.
Inman Harvey1, Ezequiel Di Paolo, Rachel Wood
1Centre for Computational Neuroscience and Robotics (CCNR), Evolutionary and Adaptive Systems Group (EASy), COGS/Informatics, University of Sussex, Brighton BN1 9QH, UK. inmanh@susx.ac.uk
Artificial Life
|April 7, 2005
Summary
Evolutionary robotics (ER) offers a novel approach to studying cognition by creating integrated sensorimotor systems. This method allows for minimal environmental coupling, providing insights into minimal models of cognition.
Area of Science:
- Robotics
- Artificial Intelligence
- Cognitive Science
Background:
- Developments in artificial neural networks, behavior-based robotics, and evolutionary algorithms paved the way for evolutionary robotics (ER) in the 1990s.
- ER is utilized as a scientific tool to investigate minimal models of cognition.
Purpose of the Study:
- To explore the motivations for employing ER in cognitive science research.
- To highlight ER's capability in generating integrated sensorimotor systems with controlled biases.
- To emphasize the importance of environmental coupling in cognitive models.
Main Methods:
- Surveying historical developments in AI and robotics.
- Examining the principles and advantages of evolutionary robotics.
- Demonstrating ER applications through case studies.
Main Results:
- ER facilitates the creation of systems that act as a whole, closely coupled with their environments.
- This approach addresses limitations in modeling cognition found in other disciplines.
- Example studies showcase ER's utility in understanding homeostasis, learning origins, and entrainment acquisition.
Conclusions:
- Evolutionary robotics provides a powerful framework for studying minimal cognition.
- The method's emphasis on integrated sensorimotor systems and environmental coupling offers unique advantages.
- ER enables the investigation of fundamental cognitive processes in a biologically plausible manner.
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