Abstract: The use of robots in society could be expanded by using reinforcement learning (RL) to allow robots to learn and adapt to new situations on-line. RL is a paradigm for learning sequential decision making tasks, usually formulated as a Markov Decision Process (MDP). For an RL algorithm to be practical for robotic control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn efficiently in the face of noise, sensor/actuator delays, and continuous state features.

Abstract: Sustainable energy systems of the future will no longer be able to rely on the current paradigm that energy supply follows demand. Many of the renewable energy resources do not produce power on demand, and therefore there is a need for new market structures that motivate sustainable behaviors by participants.

Abstract: The goal of this research is to investigate and demonstrate the capabilities of hybrid computers, combining both discrete and continuous computation, in the context of cyber-physical systems.

Abstract: This project's overall objective is to enable assertion-driven development and debugging of cyberphysical systems (CPS).   As opposed to traditional uses of assertions in software engineering, CPS demand a tight coupling of the cyber with the physical, especially to aid system validation.  This project will show how physical system models can be used to create and apply assertions to help produce methods and tools that will facilitate verification and validation of cyberphysical systems.In the first year of this project, an emp