The Siemens Advanced Microgrid Research and Demonstration Lab has been created at our Princeton campus to push the innovation envelope, to show what is possible in the realm of microgrids, and to research and demonstrate how various energy-related generation, storage and building management products behave and work together in a dynamic real-life microgrid environment. In addition, the facilities that support the living lab provide a co-creation space for public and private organizations looking to deploy highly integrated, sustainable and resilient microgrid solutions.

Two forms of artificial intelligence, Deep Learning and Reinforcement Learning (that can use Deep Learning), hold notable promise for solving such challenges because they enable robots in manufacturing systems to deal with uncertainties, to learn behaviors through interaction with their surrounding environments, and ideally generalize to new situations. Let’s take a look at how Deep Learning and Reinforcement Learning play a key role in the aforementioned use cases: flexible picking and the assembly of new components.