The impact of jointly evolving robot morphology and control on adaptation rate
Proceedings of the 11th Annual conference on Genetic and evolutionary computation, , 1769-1770, 2009
Abstract: Embodied cognition emphasizes that intelligent behavior results from the coupled dynamics between an agent's body, brain and environment. In response to this, several projects have jointly evolved robot morphology and control to realize desired behaviors. However, which aspects of a robot's morphology should be placed under evolutionary control remains an open question. Here it is shown that subjugating more of the robot's body plan to selection pressure may either slow or increase the rate of evolution, depending on the desired behavior.
More specifically, it is shown that for the legged locomotion behavior evolved and described here, increasing the number of evolved morphological parameters slows adaptation. For a more complex behavior involving legged locomotion toward an object followed by manipulation of that object, increasing the number of evolved morphological parameters accelerates adaptation. This suggests that subjugating more of the robot's design to evolution may be of increasing utility for increasingly complex tasks.
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Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).