The goal of this project is to leverage and improve upon the capabilities of honey bees as agricultural pollinators by incorporating them into Bio-Cyber Physical sistems. Rapid advances are needed to aid a dwindling agricultural, increase crop yield to sustain the growing population, and provide targeted crop care to limit the need for broad pesticide treatments. These challenges may well be addressed by autonomous mobile robots and sensor networks; unfortunately, agricultural landscapes represent vast, complex, and dynamic environments that complicate long term operation.

Consider a demand response provider that wants to learn a personalized policy for each user, but the platform faces the risk of a user abandoning the platform if she is dissatisfied with the actions of the platform. For example, the platform will want to personalize the thermostat control for the user, but faces the risk that the user unsubscribes forever if they are mistreated. We propose a general thresholded learning model for scenarios like this, and discuss the structure of optimal policies.

The goal of the proposed research is to advance the science of cyber-physical systems by more explicitly tying sensing, perception, and computing to the optimization and control of physical systems whose properties are variable and uncertain. The CPS platform to be studied is that of a bipedal robot locomoting over granular ground material with uncertain physical properties (sand, gravel, dirt, etc.).

Assistive machines such as robotic arms and powered wheelchairs promote independence and ability in those with severe motor impairments. As the field of assistive robotics progresses rapidly, these devices are becoming more capable and dextrous and as a result, higher dimensional and harder to control. The dimensionality mismatch between high-dimensional robots and low-dimensional control interfaces requires the control space to be partitioned into control modes. For full control of the robot the user switches between these partitions and this is known as mode switching.

Real world applications often naturally decompose into several sub-tasks. In many settings (e.g., robotics) demonstrations provide a natural way to specify the sub-tasks. However, most methods for learning from demonstrations either do not provide guarantees that the artifacts learned for the subtasks can be safely recombined or limit the types of composition available.

This project will design and implement a domain-specific language and compiler for microfluidic laboratory-on-a-chip (LoC) devices based on electrowetting-on-dielectric (EWoD) technology. The Lead PI’s team has designed and implemented BioScript, a domain-specific programming language for programmable microfluidics. The BioScript syntax is programmer friendly, with the intention of being accessible to biologists and other researchers and practitioners in the life sciences.

Electric vehicles (EVs) are transforming the modern transportation and energy systems.  With a growing EV market, the mass penetration of EVs into the utility grid will result in detrimental effects due to coincidence between utility peak power loading and EV charging. This impact will include increased peak loading and voltage drops that will call for over-investments in the network resulting in an overall high cost for the society.