This project develops the theory and technology for a new frontier in cyber-physical systems: cyber-physical manipulation. The goal of cyber-physical manipulation is to enable groups of hundreds or thousands of individual robotic agents to collaboratively explore an environment, manipulate objects in that environment, and transport those objects to desired locations. The project embraces realistic assumptions about the communication, computation, and sensing capabilities of simple individual robots, leading to algorithmic solutions that intrinsically leverage population size in favor of complex agents. Cyber-physical solutions for locating, grasping, and characterizing objects require tools based on distributed computational geometry, while the tasks of planning a path, initiating motion, and controlling the trajectory require tools from decentralized control and consensus. The project lays the theoretical and algorithmic foundations of cyber-physical manipulation, and proves the feasibility of the concept experimentally in hardware tests with up to 100 individual robots. The project uses the problem of manipulation as a stage on which to explore the deeper cyber-physical issue of information asymmetry; the difference in the state of the world as perceived by different agents in the system due to differences in their history of observations, and limitations in their communication capabilities. The object retrieval problem studied in this project is an elemental building block for enabling more complex cyber-physical manipulation tasks. It provides crucial algorithmic components for numerous applications of broad societal benefit, including automated construction (in which hundreds or thousands of robots fabricate large, complex structures), autonomous emergency response (in which large teams of robots find and retrieve incapacitated human survivors after a disaster), and automated environmental cleanup (in which robots secure a dangerous environment by removing debris or hazardous substances). Furthermore, distributed algorithms for multi-agent systems are of broad scientific relevance beyond the realm of cyberphysical systems. The natural world is, in its algorithmic essence, decentralized at many levels. Hence, any advancement in the understanding of how groups of individual agents collaborate to accomplish a coherent task will have broad scientific ramifications. The project has a robust educational and outreach program. One aspect is a hands-on curriculum for robotics outreach activities, called the 'Cyber-Physical Manipulation Lab.' Using a custom-designed robot platform, this educational module introduces the theory and practice of cyber-physical systems to young students to attract them to STEM subject areas at an early age. Results of the project are also incorporated into several graduate and undergraduate level courses at Rice University and Boston University.

This project focuses on the problem of information acquisition, state estimation and control in the context of cyber physical systems. In our underlying model, a (set of) decision maker(s), by controlling a sequence of actions with uncertain outcomes, dynamically refines the belief about stochastically time-varying parameters of interest. These parameters are then used to control the physical system efficiently and robustly. Here the cyber system collects, processes, and acquires information about the underlying physical system of interest, which is used for its control. The proposed work will develop a new theoretical framework for stochastic learning, decision-making, and control in stochastically-varying cyber physical systems. In order to obtain analytical insights into the structure of efficient design, we first consider the case where the actions of the cyber system only affect the estimate of the underlying physical system. This class of problems arises in the context of (distributed) sensing/tracking of a physical system in isolation from cyber system control of the physical system's state. Joint state estimation and control for cyber-physical systems will then be considered. Here the most natural first step is to obtain sufficient conditions and/or special classes of systems where a separated approach to the information acquisition and efficient control is (near) optimal. To demonstrate its utility in practice, our theoretical framework will be applied in the specific context of energy efficient control of data centers and robust control of the smart grid under limited sensing. The intellectual merit of this work will be to develop a theoretical framework for the design of cyber-physical systems including information acquisition, state estimation, and control. In addition, separation theorems for the optimality of separate state estimation and control will be explored. In terms of broader impacts, significant performance improvement of control systems closed over communication networks will impact a wide range of applications for societal benefit, including smart buildings, intelligent transportation systems, energy-efficient data centers, and the future smart-grid. The PIs plan to disseminate the research results widely through conferences and journals, as well as by organizing specialized workshops and conference sessions related to cyber physical systems. The proposed project will train Ph.D. students as well as enrich the curriculum taught by the PIs in communications, stochastic control, and networks. The PIs have a strong track record in diversity and outreach activities, which for this project will include exposure and involvement of high school and undergraduate students, including under-represented minorities and women.

This cross-disciplinary project brings together a team of engineering and computer science researchers to create, validate, and demonstrate the value of new techniques for ensuring that systems composed of combinations of hardware, software, and humans are designed to operate in a truly synergistic and safe fashion. One notable and increasingly common feature of these "Cyber-Physical-Human" (CPH) systems is that the responsibility for safe operation and performance is typically shared by increasingly sophisticated automation in the form of hardware and software, and humans who direct and oversee the behavior of automation yet may need to intervene to take over manual or shared system control when unexpected environmental situations or hardware or software failures occur. The ultimate goal is to achieve levels of safety and performance in system operation that exceed the levels attainable by either skilled human operators or completely autonomous systems acting alone. To do so, the research team will draw upon their expertise in the design of robust, fault-tolerant control systems, in the design of complexity-reduction architectures for software verification, and in human factors techniques for cognitive modeling to assure high levels of human situation awareness through effective interface design. By doing so, the safety, cost and performance benefits of increasingly sophisticated automation can be achieved without the frequently observed safety risks caused by automation creating greater distance between human operators and system operation. The techniques will be iteratively created and empirically evaluated using experimentation in human-in-the-loop simulations, including a medium-fidelity aircraft and flight simulator and a simulation of assistive automation in a medical context. More broadly, this research is expected to impact and inform the engineering of future CPH systems generally, for all industries and systems characterized by an increasing use of hardware and software automation directed and overseen by humans who provide an additional layer of safety in expected situations, Examples include highway and automotive automation, aerospace and air traffic control automation, semi-automated process control systems, and the many forms of automated systems and devices increasingly being used in medical contexts, such as the ICU and operating room. This research is also expected to inform government and industry efforts to provide safety certification criteria for the technologies used in CPH systems, and to educate a next generation of students trained in the cross-disciplinary skills and abilities needed to engineer the CPH systems of the future. The investigators will organize industry, academic, and government workshops to disseminate results and mentor students who are members of underrepresented groups through the course of this research project.

This project investigates new reinforcement learning algorithms to enable long-term real-time autonomous learning by cyber-physical systems (CPS). The complexity of CPS makes hand-programming safe and efficient controllers for them difficult. For CPS to meet their potential, they need methods that enable them to learn and adapt to novel situations that they were not programmed for. Reinforcement learning (RL) is a paradigm for learning sequential decision making processes with potential for solving this problem. However, existing RL algorithms do not meet all of the requirements of learning in CPS. Efficacy of the new algorithms for CPS is evaluated in the context of smart buildings and autonomous vehicles. Cyber-physical systems (CPS) have the potential to revolutionize society by enabling smart buildings, transportation, medical technology, and electric grids. Success of this project could lead to a new generation of CPS that are capable of adapting to their situation and improving their performance autonomously over time. In addition to the traditional methods of dissemination, this project will develop and release open-source code implementing the new reinforcement learning algorithms. Education and outreach activities associated with the project include a Freshman Research Initiative course, participation in a UT Austin annual open house that draws in many underrepresented minorities to interest the public in computer science and science in general, and the department's annual summer school for high school girls called First Bytes.

This project develops the theory and technology for a new frontier in cyber-physical systems: cyber-physical manipulation. The goal of cyber-physical manipulation is to enable groups of hundreds or thousands of individual robotic agents to collaboratively explore an environment, manipulate objects in that environment, and transport those objects to desired locations. The project embraces realistic assumptions about the communication, computation, and sensing capabilities of simple individual robots, leading to algorithmic solutions that intrinsically leverage population size in favor of complex agents. Cyber-physical solutions for locating, grasping, and characterizing objects require tools based on distributed computational geometry, while the tasks of planning a path, initiating motion, and controlling the trajectory require tools from decentralized control and consensus. The project lays the theoretical and algorithmic foundations of cyber-physical manipulation, and proves the feasibility of the concept experimentally in hardware tests with up to 100 individual robots. The project uses the problem of manipulation as a stage on which to explore the deeper cyber-physical issue of information asymmetry; the difference in the state of the world as perceived by different agents in the system due to differences in their history of observations, and limitations in their communication capabilities. The object retrieval problem studied in this project is an elemental building block for enabling more complex cyber-physical manipulation tasks. It provides crucial algorithmic components for numerous applications of broad societal benefit, including automated construction (in which hundreds or thousands of robots fabricate large, complex structures), autonomous emergency response (in which large teams of robots find and retrieve incapacitated human survivors after a disaster), and automated environmental cleanup (in which robots secure a dangerous environment by removing debris or hazardous substances). Furthermore, distributed algorithms for multi-agent systems are of broad scientific relevance beyond the realm of cyberphysical systems. The natural world is, in its algorithmic essence, decentralized at many levels. Hence, any advancement in the understanding of how groups of individual agents collaborate to accomplish a coherent task will have broad scientific ramifications. The project has a robust educational and outreach program. One aspect is a hands-on curriculum for robotics outreach activities, called the 'Cyber-Physical Manipulation Lab.' Using a custom-designed robot platform, this educational module introduces the theory and practice of cyber-physical systems to young students to attract them to STEM subject areas at an early age. Results of the project are also incorporated into several graduate and undergraduate level courses at Rice University and Boston University.

This project establishes a new framework for the formal verification of cyber-physical systems. The framework combines the power of logical decision engines and scalable numerical methods to perform safety verification of general nonlinear hybrid systems. The key difficulty with formal verification of hybrid systems is that all scalable modern verification techniques rely heavily on the use of powerful decision procedures. For hybrid systems, one needs to reason about logic formulas over the real numbers with nonlinear functions, which has been regarded as an intractable problem. The project proposes new directions for tackling the core decision problems, with the combined power of logical and numerical algorithms. The research directly leads to the development of practical tools that will push the frontier of verification of realistic cyber-physical systems to a brand new level. This project aims at fundamental research of problems that stand at the core of the design, analysis, and implementation of reliable cyber-physical systems. It combines techniques from logic, numerical analysis, and automated reasoning, and will produce a unifying methodology that is powerful to address main challenges in this field. The techniques developed in this project will significantly enhance the complexity and reliability of the next generations of cyber-physical systems. Cyber-physical systems are ubiquitous in safety-critical applications as diverse as aerospace, automotive, civil infrastructure, energy, manufacturing, and healthcare. Malfunctioning cyber-physical systems can have catastrophic economic and societal consequences. This project will have a broad range of impact in these areas. This research aims to significantly enhance the management of complexity and reliability of the next generations of cyber-physical systems, and will broadly impact all the application areas.

Cyber-physical systems employed in transportation, security and manufacturing applications rely on a wide variety of sensors for prediction and control. In many of these systems, acquisition of information requires the deployment and activation of physical sensors, which can result in increased expense or delay. A fundamental aspect of these systems is that they must seek information intelligently in order to support their mission, and must determine the optimal tradeoffs as to the cost of physical measurements versus the improvement in information. A recent explosion in sensor and UAV technology has led to new capabilities for controlling the nature and mobility of sensing actions by changing excitation levels, position, orientation, sensitivity, and similar parameters. This has in turn created substantial challenges to develop cyber-physical systems that can effectively exploit the degrees of freedom in selecting where and how to sense the environment. These challenges include high-dimensionality of observations and the associated "curse of dimensionality", non-trivial relationships between the observations and the latent variables, poor understanding of models relating the nature of potential sensing actions and the corresponding value of the collected information, and lack of sufficient training data from which to learn these models. Intellectual Merit: The proposed research includes: (1) data-driven stochastic control theory for intelligent sensing in cyber-physical systems that incorporates costs/delays/risks and accounts for scenarios where models for sensing, decision-making, and prediction are unavailable or poorly understood. (2) Validation of control methods on a UAV sensor network in the real world domain of archaeological surveying. Broader Impacts: The proposed effort includes: (a) Outreach: planned efforts for encouraging participation of women and under-represented groups; (b) Societal impact: research will lead to novel concepts in environmental monitoring, traffic surveillance, and security applications. (c) Multi- disciplinary activities: Impacting existing knowledge in cyber-physical systems, sensor management, and statistical learning. Research findings will be disseminated through conferences presentations, departmental seminars, journal papers, workshops and special sessions at IEEE CDC and RSS; (d) Curriculum development through new graduate level courses and course projects.

Cyber-physical systems are found in nearly every area of daily life: transportation, energy, medical systems, and food production. Life and safety frequently depend upon their correct operation. This project develops a novel systematic framework and methods for understanding, designing, and controlling complex coupled cyber and physical systems based on large-scale computation. This is achieved by explicitly developing the connection between the abstraction, modeling and verification frameworks of physics-based models and those of discrete-transition systems. The approach is fundamentally new, based on the unification of two recent developments: (1) new probabilistic tools for simulating and analyzing high-fidelity physics-based models; and (2) statistical model checking methods. In addition to analytical research, the project produces methods and computational tools that can be used on a wide range of cyber-physical systems, particularly those that are safety and performance critical. There have been dramatic recent advances in probabilistic computational techniques for purely physics-based models, treating them computationally as Markov chains, and enabling efficient computation even for high-dimensional systems. Simultaneously with these purely physics-based model approaches, state-of-the-art methods for the verification of purely discrete-state systems have been developed based on stochastic computational tools also using Markov chains as the basis. This project connects these two independent branches to yield a radically new approach for complex, high-dimensional cyber-physical systems, based on the unifying concept of Markov models as an interface between the cyber and physical domains. An integral part of the project is a unified educational program aimed at addressing key bottlenecks in the recruitment and development of female and minority students into engineering and computer science. The educational program is developed around a new robotic vehicle with complex fluid-structure dynamics, that is used in: (i) a week-long residential summer camp for female high-school students on "Mechanics & Dynamics"; (ii)undergraduate research experiences for female and minority students to facilitate the transition to graduate education; and (iii) an experimental graduate course on verification of embedded systems.

Cyber-Physical Systems (CPS) encompass a large variety of systems including for example future energy systems (e.g. smart grid), homeland security and emergency response, smart medical technologies, smart cars and air transportation. One of the most important challenges in the design and deployment of Cyber-Physical Systems is how to formally guarantee that they are amenable to effective human control. This is a challenging problem not only because of the operational changes and increasing complexity of future CPS but also because of the nonlinear nature of the human-CPS system under realistic assumptions. Current state of the art has in general produced simplified models and has not fully considered realistic assumptions about system and environmental constraints or human cognitive abilities and limitations. To overcome current state of the art limitations, our overall research goal is to develop a theoretical framework for complex human-CPS that enables formal analysis and verification to ensure stability of the overall system operation as well as avoidance of unsafe operating states. To analyze a human-CPS involving a human operator(s) with bounded rationality three key questions are identified: (a) Are the inputs available to the operator sufficient to generate desirable behaviors for the CPS? (b) If so, how easy is it for the operator with her cognitive limitations to drive the system towards a desired behavior? (c) How can areas of poor system performance and determine appropriate mitigations be formally identified? The overall technical approach will be to (a) develop and appropriately leverage general cognitive models that incorporate human limitations and capabilities, (b) develop methods to abstract cognitive models to yield tractable analytical human models (c) develop innovative techniques to design the abstract interface between the human and underlying system to reflect mutual constraints, and (d) extend current state-of-the-art reachability and verification algorithms for analysis of abstract interfaces, iin which one of the systems in the feedback loop (i.e., the user) is mostly unknown, uncertain, highly variable or poorly modeled. The research will provide contributions with broad significance in the following areas: (1) fundamental principles and algorithms that would serve as a foundation for provably safe robust hybrid control systems for mixed human-CPS (2) methods for the development of analytical human models that incorporate cognitive abilities and limitations and their consequences in human control of CPS, (3) validated techniques for interface design that enables effective human situation awareness through an interface that ensures minimum information necessary for the human to safely control the CPS, (4) new reachability analysis techniques that are scalable and allow rapid determination of different levels of system safety. The research will help to identify problems (such as automation surprises, inadequate or excessive information contained in the user interface) in safety critical, high-risk, or expensive CPS before they are built, tested and deployed. The research will provide the formal foundations for understanding and developing human-CPS and will have a broad range of applications in the domains of healthcare, energy, air traffic control, transportation systems, homeland security and large-scale emergency response. The research will contribute to the advancement of under-represented students in STEM fields through educational innovation and outreach. The code, benchmarks and data will be released via the project website. Formal descriptions of models of human cognition are in general incompatible with formal models of the Cyber Physical System (CPS) the human operator(s) control. Therefore, it is difficult to determine in a rigorous way whether a CPS controlled by a human operator will be safe or stable and under which circumstances. The objective of this research is to develop an analytic framework of human-CPS systems that encompasses engineering compatible formal models of the human operator that preserve the basic architectural features of human cognition. In this project the team will develop methodologies for building such models as well as techniques for formal verification of the human-CPS system so that performance guarantees can be provided. They will validate models in a variety of domains ranging from air traffic control to large scale emergency response to the administration of anesthesia.

To ensure operational safety of complex cyber-physical systems such as automobiles, aircraft, and medical devices, new models, analyses, platforms, and development techniques are needed that can predict, possible interactions between features, detect them in the features' concrete implementations, and either eliminate or mitigate such interactions through precise modeling and enforcement of mixed-criticality cyber-physical system semantics. This project is taking a novel approach to reasoning about and managing feature interactions in cyber-physical systems, which encompasses interactions within software, interactions through the physical dynamics of the system, and interactions via shared computational resources. The proposed approach consists of three tightly coupled research thrusts: (1) a novel way of modeling features as automata equipped with both physical dynamics of the feature environment, and an assigned criticality level in each state of an automaton, (2) new automata-theoretic and control-theoretic analysis techniques, enabled by the modeling approach, and (3) new algorithms for adaptive sharing of computational resources between individual features that are guaranteed to satisfy the assumptions made during analysis, realized within a novel mixed-criticality cyber-physical platform architecture. The modeling approach will introduce a new model for mixed-criticality cyber-physical components and will support modern development standards, such as AUTOSAR in the automotive industry, for assigning criticality levels to features. Component interfaces in this model will capture control modes and the associated physical dynamics, operating modes and the associated resource requirements and criticality level, as well as relationships between control modes and operating modes. Analysis of features expressed in the proposed model will include detection of interactions and exploration of their effect on safety properties of the composite system. The broader impacts of the proposed work are twofold. One impact lies in the pervasive use of cyber-physical systems in our society. If the developed results are adopted in industry, it may help to promote improved safety of such systems. Results of the proposed research will be used in courses offered at both University of Pennsylvania and Washington University at the graduate and undergraduate levels. The project will also provide students with opportunities to get involved in cutting edge research within their fields of study.