CPS: Medium: Learning for Control of Synthetic and Cyborg Insects in Uncertain Dynamic Environments

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The objective of this research is to enable operation of synthetic andcyborg insects in complicated environments, such as outdoors or in acollapsed building. As the mobile platforms and environment havesignificant uncertainty, learning and adaptation capabilities arecritical. The approach consists of three main thrusts to enable thedesired learning and adaptation: (i) Development of algorithms toefficiently learn optimal control policies and dynamics models throughsharing the learning and adaptation between various instantiations ofplatforms and environments. (ii) Creation of control learningalgorithms which can be run on low-cost, low-power mobile platforms.(iii) Development of algorithms for online improvement of policyperformance in a minimal number of real-world trials. The proposed research will advance learning and adaptationcapabilities of practical cyberphysical systems. The proposedapproach will be generally applicable and lead to a new class oflearning and adapting systems that are able to leverage sharedproperties between multiple tasks to significantly speed uplearning and adaptation. Success in this research project will bring society closer to solvingthe grand challenge of teams of mobile, disposable, search and rescuerobots which can robustly locomote through uncertain and novelenvironments, finding survivors in disaster situations, while removingrisk from rescuers. This project will provide interdisciplinarytraining through research and classwork for undergraduate and graduatestudents in creating systems which intimately couple the cyber andphysical aspects in robotic and living mobile platforms. Through theSUPERB summer program, under-represented students in engineering willexperience research in learning and robotics.