Abstract: One of the open problems in autonomous robotics is how to consistently and scalably integrate new behaviors into a robot with an existing behavioral repertoire. In this work a new technique called behavior chaining is introduced, which allows for gradually expanding the behavioral repertoire of a dynamically behaving robot. The approach relies heavily on scaffolding: gradually restructuring the robot’s environment such that selection pressure favors the incorporation of a new behavior. This method teaches a robot a compound behavior not yet reported in the literature: dynamic legged locomotion toward an object followed by grasping, lifting and holding of that object in a physically-realistic three-dimensional environment. The method assumes that success is dependent on the order in which behaviors are learned. This is justified by results which show that if a robot is forced to learn lifting first and then incorporate locomotion, it eventually succeeds at both more often than a robot forced to learn locomotion first and then lifting.
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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).