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Adrian Agogino: "Machine Learning for Slow but Steady Interplanetary Construction."

Abstract: For prolonged manned missions to destinations such as the moon and Mars, there is a need for significant infrastructure construction ahead of time, such as habitats and landing pads. Unfortunately we have little experience in remote construction and using conventional methods is likely to be expensive, cumbersome and unreliable. Fortunately these challenges may be overcome by taking advantage of the long lead time for such missions and using teams of small and slow construction robots. We propose using teams of simple autonomous robots for this purpose that would perform continuous construction over a period of many years or even decades. While individual robot reliability will be low over such long time frames, system reliability will be maintained by using machine learning over simulations to achieve coordination and reconfigurations in the event of lost robots.

Bio: Dr. Adrian Agogino is a research scientist at NASA Ames Research Center. His interests are in the fields of complex system control, tensegrity robotics, rocket analysis and multiagent control. He is one of the foremost experts in multiagent coordination of air traffic flow and complex control of robots based on tensegrity structures, successfully utilizing evolution algorithms and reinforcement learning to achieve complex behaviors. He has over 70 publications in the fields of machine learning, soft robotics, rocket analysis, multiagent systems, reinforcement learning, evolutionary systems and visualization of complex systems. He has received awards for publications in both learning and in visualization. Since 2004 he has worked for The University of California Santa Cruz, working on complex systems projects including air traffic flow management, rocket engine analysis, robotics and coordination in multi-rover learning.

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