Automated Synthesis of Body Schema using Multiple Sensor Modalities
Proc. of the Int. Conf. on the Simulation and Synthesis of Living Systems (ALIFEX), , , 2006
Abstract: The way in which organisms create body schema, based on their interactions with the real world, is an unsolved problem in neuroscience. Similarly, in evolutionary robotics, a robot learns to behave in the real world either without re-course to an internal model (requiring at least hundreds of interactions), or a model is hand designed by the experimenter (requiring much prior knowledge about the robot and its environment). In this paper we present a method that allows a physical robot to automatically synthesize a body schema, using multimodal sensor data that it obtains through interaction with the real world. Furthermore, this synthesis can be either parametric (the experimenter provides an approximate model and the robot then refines the model) or topological: the robot synthesizes a predictive model of its own body plan using little prior knowledge. We show that this latter type of synthesis can occur when a physical quadrupedal robot performs only nine, 5-second interactions with its environment.
[edit database entry]
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).