Abstract: No robots at the sub-­‐cm3 scale exist because their development faces a number of open challenges. This project focuses on identifying and determining means for solving these challenges.

Abstract: We wish to create office divider/façade systems that can adapt their visual appearance and privacy properties to your working needs, can be rearranged within minutes, and allow you to program them by gestures. For example, a wall could sense outside weather and its occupants’ moods and adapt its color accordingly. Or, a wall could provide its user with privacy by becoming opaque and emitting white noise. Finally, individual elements could open themselves up to let air, noise, and little objects pass between zones.

Abstract: This project is to develop dynamical models of distributed computation systems that are resilient to noise, unreliable communication and other source uncertainty. The key idea is centered on the development of optimization systems. These are dynamical systems that (solve) stabilize around the optimal solution of a (static) convex optimization problem. If the optimization problem is separable, then the designed dynamic system decomposes into a set of locally interacting dynamic systems.

Abstract: We are designing lane merging systems that assist drivers to maneuver safely. The driver signals his intent to change lanes. The cars communicate and create a safe gap. The driver is notified when it is safe to merge, or when a condition occurs that makes a lane change dangerous.

Abstract: The CYPRESS (CYber Physical RESiliance and Sustainability) project explores techniques for dependability and resilience in instrumented cyberphysical spaces (ICPS) where physical spaces have been instrumented with “intelligence” through heterogeneous sensing, actuation and communication mechanisms.

Abstract: This project addresses architectural considerations in the design and operation of future power grids. This includes the consideration of sophisticated sensing, communication, and actuation capabilities on the system's reliability, price volatility, and economic and environmental efficiency.

Abstract: Modern processor architectures sacrifice timing predictability for increased average computational throughput. Branch prediction, multi-level memory hierarchies, out-of-order execution, and data forwarding all make accurate execution time predictions impossible. As accurate timing predictions are required for task scheduling, our goal is the development of a Precision Timed (PRET) processor, along with the inter-process communication methods and operating system services, which remove many of the sources of timing indeterminacy.

Abstract: Wireless sensor-actuator networks (WSAN) are systems consisting of numerous sensing and actuation devices that interact with the environment and coordinate their activities over a wireless communication network. WSANs represent an important class of cyber-physical system (CPS) found in the national power grid and air/traffic networks. This project addresses the issue of resilience in WSANs.

Abstract: We are developing a new computational framework and physical platform for modeling, analyzing, and designing dense networks of micro-robotic swarms. The physical platform is based on a bio-hybrid micro-robotic approach, in which bacteria serve as on- board actuators. The micro-robots are controlled through passive (e.g. chemical gradients) and active (e.g. magnetic fields) steering mechanisms. Here, we present the first step towards passive control by characterizing the chemotactic behavior of free- swimming bacteria.

Abstract:  Reinforcement Learning (RL) is a paradigm for learning decision- making tasks that could enable cyber-physical systems (CPS) to learn and adapt to situations on-line. For an RL algorithm to be practical for CPS control tasks, it must learn in very few samples, while continually taking actions in real-time. In addition, the algorithm must learn effi- ciently in the face of noise, sensor/actuator delays and continuous state features. We describe TEXPLORE, a model-based RL method that ad- dresses these issues.