The objective of this research is to develop new principles for creating and comparing models of skilled human activities, and to apply those models to systems for teaching, training and assistance of humans performing these activities. The models investigated will include both hybrid systems and language-based models. The research will focus on modeling surgical manipulations during robotic minimally invasive surgery. Models for expert performance of surgical tasks will be derived from recorded motion and video data. Student data will be compared with these expert models, and both physical guidance and information display methods will be developed to provide feedback to the student based on the expert model. The intellectual merit of this work lies in the development of a new set of mathematical tools for modeling human skilled activity. These tools will provide new insights into the relationship between skill, style, and content in human motion. Additional intellectual merit lies in the connection of hybrid systems modeling to language models, the creation of techniques for automated training, and in the assessment of new training methods. The broader impact of this research will be the creation of automated methods for modeling and teaching skilled human motion. These methods will have enormous implications for the training and re-training of the US workforce. This project will also impact many diversity and outreach activities, including REU programs and summer camps for K-12 outreach. The senior personnel of this project also participate in the Robotic Systems Challenge and the Women in Science and Engineering program.

The objective of this research is to develop principles and tools for the design of control systems using highly distributed, but slow, computational elements. The approach of this research is to build an architecture that uses highly parallelized, simple computational elements incorporating nonlinearities, time delay and asynchronous computation as integral design elements. Tools for the design of non-deterministic protocols will be developed and demonstrated using an existing multi-vehicle testbed at Caltech. The motivation for using "slow computing" is to develop new feedback control architectures for applications where computational power is extremely limited. Examples of such systems are those where the energy usage of the system must remain small, either due to the source of power available (e.g. batteries or solar cells) or the physical size of the device (e.g. microscale and nanoscale robots). A longer term application area is in the design of control systems using novel computing substrates, such as biological circuits. A critical element in both cases is the tight coupling between the dynamics of the underlying process and the temporal properties of the algorithm that is controlling it. The implementation plan for this project involves students from multiple disciplines (including bioengineering, computer science, electrical engineering and mechanical engineering) as well as at multiple experience levels (sophomores through PhD students) working together on a set of interlinked research problems. The project is centered in the Control and Dynamical Systems department at Caltech, which has a strong record of recruiting women and underrepresented minority students into its programs.

The objective of this research is to enable improved performance and application development efficiency for streaming applications. The approach is to use architecturally diverse computing engines, such as field programmable gate arrays and graphics processing units, to execute portions of an application. This approach is especially well suited to applications that process streaming data, such as from a sensor array like a telescope or other scientific instrument. Intellectual merit. While the use of architecturally diverse systems has a long history in the world of performance-critical systems that interact with the physical world, application development has always been a challenge. This proposal seeks to improve application development by making significant progress in two essential areas: performance monitors and debuggers. The approach has the following properties: it is non-intrusive with respect to the application being monitored and it supports data collection concerning rare events. In particular, it allows correlation of the cyber portion of an application with the physical portion in which the rare events occur. Broader impacts. Impact on society: The research can have a dramatic impact on cyber-physical computing applications, from scientific instrumentation to medical imaging. Improvements in power efficiency, weight, and volume are all achievable through the use of architectural diversity. Education: The proposed research will be carried out by graduate and undergraduate students, both furthering their education and increasing the nation's trained workforce. Underrepresented groups: Washington University's Chancellor's Fellowship and Olin Fellowship programs will be leveraged to attract the participation of individuals from underrepresented groups.

The objective of this research is to develop an integrated methodology for control system design in situations where disturbances primarily result from routine human behavior, as, for example, in future artificial pancreas systems where meals and exercise are the main disturbances affecting blood glucose concentration. The approach is to recognize that human behavioral disturbances (i) are generally random but cannot be treated as zero-mean white noise processes and (ii) occur with statistical regularity but cannot be treated as periodic due to natural variation in human behavior. This emerging class of problems requires (i) the derivation of new mathematical representations of disturbances for specific applications and (ii) the formulation of new stochastic control models and algorithms that exploit statistical regularity in the disturbance process. The intellectual merit of the proposed research stems from the fact that it explicitly recognizes a new class of disturbances, human behavioral disturbances, seeking to develop an integrated approach to statistically characterizing and responding to future perturbations, adapting gracefully to uncertainty about the future. The anticipated research outcomes will be relevant in diverse fields, including stochastic hybrid control and human automation interaction. As a broader implication, the proposed research will enable the design of future field deployable artificial pancreas systems, potentially improving the lives of 1.5 million Americans suffering from Type 1 diabetes. With help from the two graduate students funded by the project, the principle investigator will supervise a Capstone design course, exposing undergraduates to various aspects of control under human behavioral disturbances.

This objective of this proposal is to improve the management of the air traffic system, a cyber-physical system where the need for a tight connection between the computational algorithms and the physical system is critical to safe, reliable and efficient performance. The approach is based on an adaptive multiagent coordination algorithm with a particular emphasis on the systematic selection of the agents, their actions and the agents' reward functions. The intellectual merit lies in addressing the agent coordination problem in a physical setting by shifting the focus from "how to learn" to "what to learn." This paradigm shift allows a separation between the learning algorithms used by agents, and the reward functions used to tie those learning systems into system performance. By exploring agent reward functions that implicitly model agent interactions based on feedback from the real world, this work aims to build cyber-physical systems where an agent that learns to optimize its own reward leads to the optimization of the system objective function. The broader impact is in providing new air traffic flow management algorithms that will significantly reduce air traffic congestion. The potential impact cannot only be measured in currency ($41B loss in 2007) but in terms of improved experience by all travelers, providing a significant benefit to society. In addition, the PIs will use this project to train graduate and undergraduate students (i) by developing new courses in multiagent learning for transportation systems; and (ii) by providing summer internship opportunities at NASA Ames Research Center.

The objective of this research is to develop new scientific and engineering principles, algorithms and models for the design of battery powered cyber-physical systems whose computational substrates include high-performance multiprocessor systems-on-chip. The approach is to design control tasks that guarantee performance and meet criteria for battery operation time. Task schedulers are co-designed to balance the computing load across the multiple processors, and to control the physical plant together with the control tasks. The controller and scheduler will be integrated with battery management algorithms through a systems theory approach so that the methods are provably correct with justfiable performance. Intellectual Merit: The program will create progress in digital and hybrid control theory that keeps up with the recent trend of using multiprocessor systems-on-chips for control and robotic applications. The mechanism for the migration of control tasks between multiple processors will respect physical and thermal performance. A novel battery dynamic discharge model is developed, which may be applied to context when the discharge current of batteries cannot be predicted by existing static battery models. Broader Impacts: Collaborations with industrial partners have been set up. The program offers multidisciplinary training in cyber-physical systems. A teaching and outreach lab is in place to host K-12 student teams that participate in robot competitions, and has become an Explorer Post for Boy Scout of America.

The objective of this research is to develop technologies to improve the efficiency and safety of the road transportation infrastructure. The approach is to develop location-based vehicular services combining on-board automotive computers, in-car devices, mobile phones, and roadside monitoring/surveillance systems. The resulting vehicular Cyber Physical Systems (CPS) can reduce travel times with smart routing, save fuel and reduce carbon emissions by determining greener routes and commute times, improve safety by detecting road hazards, change driving behavior using smart tolling, and enable measurement-based insurance plans that incentivize good driving. This research develops distributed algorithms for predictive travel delay modeling, feedback-based routing, and road hazard assessment. It develops privacy-preserving protocols for capturing and analyzing data and using it for tasks such as congestion-aware tolling. It also develops a secure macro-tasking software run-time substrate to ensure that algorithms can be programmed centrally without explicitly programming each node separately, while ensuring that it is safe to run third-party code. The research focuses on re-usable methods that can benefit multiple vehicular services, and investigates which lessons learned from this vehicular CPS effort generalize to other situations. Road transportation is a grand challenge problem for modern society, which this research can help overcome. Automobile vendors, component developers, and municipal authorities have all shown interest in deployment. The education plan includes outreach to local K-12 students and a new undergraduate course on transportation from a CPS perspective, which will involve term projects using the data collected in the project

The objective of this research is to create computational foundation, methods, and tools for efficient and autonomous optical micromanipulation using microsphere ensembles as grippers. The envisioned system will utilize a holographic optical tweezer, which uses multiple focused optical traps to position microspheres in three-dimensional space. The proposed approach will focus on the following areas. First, it will provide an experimentally validated optical-tweezers based workstation for concurrent manipulation of multiple cells. Second, it will provide algorithms for on-line monitoring of workspace to support autonomous manipulation. Finally, it will provide real-time image-guided motion planning strategies for transporting microspheres ensembles. The proposed work will lead to a new way of autonomously manipulating difficult-to-trap or sensitive objects using microspheres ensembles as reconfigurable grippers. The proposed work will also lead to fundamental advances in several cyber physical systems areas by providing new approaches to micromanipulations, fast and accurate algorithms with known uncertainty bounds for on-line monitoring of moving microscale objects, and real-time motion planning algorithms to transport particle ensembles. The ability to quickly and accurately manipulate individual cells with minimal training will enable researchers to conduct basic research at the cellular scale. Control over cell-cell interactions will enable unprecedented insights into cell signaling pathways and open up new avenues for medical diagnosis and treatment. The proposed integration of research with education will train students with a strong background in emerging robotics technologies and the inner workings of cells. These students will be in a unique position to rapidly develop and deploy specialized robotics technologies.

The physical environment of a cyber-physical system is unboundedly complex, changing continuously in time and space. An embodied cyber-physical system, embedded in the physical world, will receive a high bandwidth stream of sensory information, and may have multiple effectors with continuous control signals. In addition to dynamic change in the world, the properties of the cyber-physical system itself ? its sensors and effectors ? change over time. How can it cope with this complexity? The hypothesis behind this proposal is that a successful cyber-physical system will need to be a learning agent, learning the properties of its sensors, effectors, and environment from its own experience, and adapting over time. Inspired by human developmental learning, the assertion is that foundational concepts such as Space, Object, Action, etc., are essential for such a learning agent to abstract and control the complexity of its world. To bridge the gap between continuous interaction with the physical environment, and discrete symbolic descriptions that support effective planning, the agent will need multiple representations for these foundational domains, linked by abstraction relations. To achieve this, the team is developing the Object Semantic Hierarchy (OSH), which shows how a learning agent can create a hierarchy of representations for objects it interacts with. The OSH shows how the ?object abstraction? factors the uncertainty in the sensor stream into object models and object trajectories. These object models then support the creation of action models, abstracting from low-level motor signals. To ensure generality across cyber-physical systems, these methods make only very generic assumptions about the nature of the sensors, effectors, and environment. However, to provide a physical test bed for rapid evaluation and refinement of our methods, the team has designed a model laboratory robotic system to be built from off-the-shelf components, including a stereo camera, a pan-tilt-translate base, and a manipulator arm. For dissemination and replication of research results, the core system will be affordable and easily duplicated at other labs. There are plans to distribute the plans, the control software, and the software for experiments, to encourage other labs to replicate and extend the work. The same system will serve as a platform for an open-ended set of undergraduate laboratory tasks, ranging from classroom exercises, to term projects, to independent study projects. There is a preliminary design for a very inexpensive version of the model cyberphysical system that can be constructed from servo motors and pan-tilt webcams, for use in collaborating high schools and middle schools, to communicate the breadth and excitement of STEM research.

The objective of this research is to enable operation of synthetic and cyborg insects in complicated environments, such as outdoors or in a collapsed building. As the mobile platforms and environment have significant uncertainty, learning and adaptation capabilities are critical. The approach consists of three main thrusts to enable the desired learning and adaptation: (i) Development of algorithms to efficiently learn optimal control policies and dynamics models through sharing the learning and adaptation between various instantiations of platforms and environments. (ii) Creation of control learning algorithms which can be run on low-cost, low-power mobile platforms. (iii) Development of algorithms for online improvement of policy performance in a minimal number of real-world trials. The proposed research will advance learning and adaptation capabilities of practical cyberphysical systems. The proposed approach will be generally applicable and lead to a new class of learning and adapting systems that are able to leverage shared properties between multiple tasks to significantly speed up learning and adaptation. Success in this research project will bring society closer to solving the grand challenge of teams of mobile, disposable, search and rescue robots which can robustly locomote through uncertain and novel environments, finding survivors in disaster situations, while removing risk from rescuers. This project will provide interdisciplinary training through research and classwork for undergraduate and graduate students in creating systems which intimately couple the cyber and physical aspects in robotic and living mobile platforms. Through the SUPERB summer program, under-represented students in engineering will experience research in learning and robotics.