Gaurav S. Sukhatme - US grants

The Multihop wireless capabilities will enable communication and coordination among autonomous nodes in unplanned environments and configurations. At the same time wireless channels present challenges of dynamic operating conditions, power constraints for autonomously-powered nodes, and complicating interactions between high level behavior and lower level channel characteristics (e.g. , increased synchronized communication will significantly degrade channel characteristics). The major goal of the research is the development, testing and characterization of algorithms for scalable, application driven, wireless network services using a heterogeneous collection of communicating mobile nodes. Some of these nodes will be autonomous (robots) in that their movements will not be human controlled. The others will be portable thus, making them dependent on humans for transportation. While the focus of the work is on the mobile nodes, include immobile computers on the network as well. It emphasize that most (though not all)
of the mobile nodes will have modest sensing, computational and communication resources. The collection as a whole is an example of a sensor network. The chief scientific motivation behind the work is the design of robust, efficient and scalable algorithms. The researchers hypothesize that distributed algorithms that rely on local interactions have many compelling characteristics, resulting in these properties. There is significant overlap between the problems of coordinating the autonomous mobile nodes that carry some of the sensors and the algorithms that direct the f ow of information from source(s) to sink(s) in the network. Both setsof algorithms will be carefully designed to improve robustness, effciency and scalability.

EIA-0121141
Requicha, Aristides
University of Southern California

ITR/SI+AP: Active Sensor Networks with Applications in Marine Microorganism Monitoring





The proposed research combines networking, distributed robotics, nanorobotics, and microbiology in an effort to develop and apply technology for the in-situ, real-time monitoring of microbial populations in aquatic environments, such as the ocean or water supply systems. The application context provides feedback from experiments with realistic systems, and this feedback is essential to the progress of the Information Technology (IT) research proposed here. This project addresses two key challenges for IT during this decade: moving from virtual to physical applications, and moving from macro to micro and nano.

The IT focus is on the study of Physically-Coupled Scalable Information Infrastructures (PCSIIs), which effectively "embed the internet". The sensors and actuators in the proposed PCSII must have small physical dimensions, comparable to those of the microorganisms to be monitored. They must be deployed in very large numbers to achieve the unprecedented spatial and temporal resolution necessary to investigate the causal relationships between environmental conditions and microorganisms. Control and coordination of a multitude of such devices of limited and heterogeneous capabilities raise major challenges for networking, distributed coordination and distributed algorithms. Sensing for detection and identification of microorganisms is another challenge, which will be tackled by using nanorobotic Scanning Probe Microscope technology.

The project focuses on robotic tasks that cannot be done by a single robot, with the goal of developing multi-robot systems with increased fault tolerance and task speedup. This domain will be studied formally, in pursuit of a fundamental theory. The resulting formalism is to guide researchers in the design of experiments with large-scale multi-robot systems. Macroscopic models using expected (rather than exact) values of the system states will be explored in the search of a theory of multi-robot teams. Models of large-scale robot teams will be designed, and testable hypotheses about system performance will be formulated. Scalable algorithms for certain canonical tasks in a planar environment will be designed and tested, using a large group of mobile robots and a multi-robot simulator. The education part of the project is closely tied to the PI's research, and includes a new teaching lab, lab courseware development, and hosting high-school minority summer interns at the lab.

Structural Health Monitoring (SHM) is a highly interdisciplinary area of research focused on developing techniques to detect damage in structures such as buildings, bridges, aircraft, ships and spacecraft. Most SHM research to date has focused either on global damage assessment techniques using low-resolution measurements of a structure's response to ambient excitation, or on limited local independent damage detection mechanisms.

This proposal advocates a paradigm shift in SHM, using decentralized local excitation and high-resolution measurements of response to these excitations, detected and collaboratively analyzed through a spatially
dense wireless network of devices. This shift promises simpler and more accurate techniques to identify and even localize damage within the structure.

The goal of the proposed research is the design of a networked computer system, with distributed actuation and sensing, for SHM. The term "networked SHM" denotes the class of monitoring systems that will
be enabled by this research. By combining local excitation with high-resolution sensing, networked SHM is quite distinct from other sensor network applications being examined today. Networked SHM promises a future where, for example, buildings are constructed using concrete mixed with several tens of thousands of embedded sensor devices as well as low-power local exciters. The network of sensors will be able to continuously monitor the structure, trigger alarms that identify the onset of damage, precisely pinpoint the location of damage and also provide a long-term history of ambient stresses imposed on the building.

Computer systems are becoming pervasive as well as growing rapidly in scale, complexity, distribution, and heterogeneity. For these reasons, it is becoming increasingly important for engineers to reason quantitatively about the various systems' properties of interest and curb their design, development, and deployment costs. In an ideal situation important system characteristics, such as performance and reliability, would be assessed at system design time, before significant time and cost have been devoted to a project. However, making useful (quantitative) predictions in early design stages is difficult at best, due ti the interplay between many relevant factors, such as complex properties of software components, the potential effects on software of the firmware (hardware, OS, device drivers), as well as the potentially conflicting desired system attributes.

Attacking even a small subset of these problems is challenging enough and would result in significant advances to the state of the art in complex systems engineering. Hence, this project proposes to focus efforts on design-time evaluation of architectures with respect to one key attribute - reliability. Here, reliability is defined as the probability that the system will perform its intended functionality under specified design limits. The proposed approach will enable an engineer to build a multi-faceted, hierarchical model of a system and assess its reliability in an incremental, scalable fashion. Although several software reliability techniques exist, they are insufficient. To address these deficiencies, the project will develop a technique that will couple software architectural models (well understood by system designers) with augmented Hidden Markov Models (which allow us to reason about numerous uncertainties existing in early design phases), and will augment this methodology with the relevant attributes of the firmware in support of more complete and meaningful reliability models.

The project will evaluate the results the methods developed along two measures of interest: tractability (intended to address scalability issues existing in real, complex systems) and sensitivity (intended to address issues of confidence in the researchers predictions under numerous uncertainties existing at design time) and will apply the results to real problems from the domain of mobile robotics, a problem domain that is representative of many complex, distributed, and embedded systems.

Mobile sensor networks in which some nodes can move (on their own or through the agency of others) combine advanced concepts in perception, communication, and control to create computational systems capable of interacting in meaningful ways with the physical environment. We are motivated by the vision of providing support for communication, monitoring, and surveillance over areas that lack the infrastructure for traditional computation and communication (e.g. protection and monitoring for large geographical areas such as the Alaska pipeline). By deploying a sensor network along such areas (using humans or flying drones) one can maintain a global view of key attributes of the environment. This research focuses on the following three goals within the broad area of synergy between communication and mobility in sensor networks: 1. Deployment - Distributed algorithms for mobility-assisted network deployment with desired topological properties, 2. Mobility-based Topology Control - Distributed algorithms for adaptive self-organization of the network nodes to support desired information flow, and 3. Link-level Communication Modeling - the development and analysis of a suite of localized algorithms that monitor the connectivity properties of the network locally and give global link characterization for the entire network. We expect this research to impact the deployment and maintenance of real-world sensor-actuator networks in the future via a deep understanding of the fundamental problems outlined above. The educational impact of the project will be the creation of a multi-purpose teaching module using the experimental systems developed in the proposed research.

Observing systems facilitate scientific studies by instrumenting the real world and collecting corresponding measurements, with the aim of detecting and tracking phenomena of interest. In this proposal, we focus on a class of observing systems which are (1) embedded into the environment, (2) consist of stationary and mobile sensors, and (3) react to collected observations by reconfiguring the system and adapting which observations are collected next, these are referred to as Reactive Observing Systems (ROS). The goal of ROS is to help scientists verify or falsify hypotheses with useful samples taken by the stationary and mobile units, as well as to analyze data autonomously to discover interesting trends or alarming conditions.
A wide range of critical environmental monitoring objectives in resource management, environmental protection, and public health all require distributed observing systems. This project will explore ROS in the context of a marine biology application, where the system monitors, e.g., water temperature and light as well as concentrations of micro-organisms and algae in a body of water. Using a hybrid network of stationary and mobile sensors, communicating both via wired and wireless links, the system collects fine-grained measurements of interesting information in near real-time. An example of the use of such a system is the rapid identification of micro-organisms to predict the onset of algal blooms. Such blooms can have devastating economic consequences.

Current technology precludes sampling all possibly relevant data. Therefore there is need to develop approaches for optimizing and controlling the set of samples to be taken at any given time, taking into consideration the application's objectives and system resource constraints. To support such an optimization and control process, a significant part of the framework must be dedicated to the development of models of data, and their automatic validation or adaptation. As part of the validation and adaptation process, the framework must also include a distributed support mechanism for locating data of interest. The methods to be pursued in the project include a multi-scale modeling framework for ROS, that allows applications to construct inter-related models of varying spatio-temporal scope based on collected data. Guided by the models, the reactive elements of the system predict where interesting data and phenomena are likely to be found. In the process of constructing models, the system actively seeks most useful data to improve both, the models and phenomenon detection and tracking. In a feedback cycle, this data acquisition is guided by previous, perhaps less precise, models. Thus, the system to be developed (AMBROsia) enables optimal collection of measurements in a manner that respects system resource constraints, yet improves the overall fidelity of phenomenon detection and tracking. Such a system will aid scientific research by facilitating the testing of scientific hypothesis. It will provide timely predictions of sampling needs, and tracking information for dynamic phenomena. Overall, AMBROSia will facilitate observation, detection, and tracking of scientific phenomena that were previous only partially (or not at all) observable and/or understood.

This project, acquiring a mobile humanoid robotics platform as the centerpiece of a Human-Centered Robotics Lab, aims at assisting a broad population in need, based on the belief that the most suitable form of multi-purpose assistive machine for humans will be human-like. This new kind of robot, not highly accurate, stationary, single task machine with sensing abilities as for typical industrial applications, is richly equipped with multi-model sensing, a high level of dexterity, compliance for safe operation, and mobility. Endowed with the appearance and behavior of a social system appropriate for human environments, it can perform a large number of assistive tasks, autonomously or in collaborative instruction with humans. A humanoid robot instigates a variety of original research. Developing humanoid behavior advances robotics and automation technology while promoting interdisciplinary interaction with natural sciences

This is funding to support a PI Workshop on the campus of the University of Southern California in Los Angeles, on September 27-28, 2006. HRI is one of the two cross-cutting technical areas defined in IIS Division's new solicitation entitled "Information and Intelligent Systems: Advancing Human-Centered Computing, Information Integration and Informatics, and Robust Intelligence" (NSF 06-572). The 1.5-day workshop will bring together current NSF PIs with ongoing HRI research programs, along with selected additional members of the research community with related interests, to help NSF identify emerging trends in this rapidly evolving field. Participants will discuss and prioritize the important subfields of HRI that the scientific community believes will have a major impact in the near-to-medium term. This workshop is one of several similar events, each focusing on a particular aspect of the new solicitation, being sponsored by IIS Division with the goal of encouraging the research community to provide input to NSF in its strategic planning process as we prepare for the challenges and opportunities anticipated during the coming period of explosive technological growth and change.

Broader Impacts: This workshop will present a unique opportunity for NSF PIs and other members of the rapidly growing human-robot interaction (HRI) research community to discuss the state of the art and to engage in strategic planning toward shaping the direction of this new and interdisciplinary research area with major implications on the future of robotics in society. The workshop organizers will produce a comprehensive workshop report, and the workshop website will become a permanent repository of the workshop discussions, breakout group reports, etc for general access by the research community at large.

IIS - 0755534
Title: REU Site: Coordination, Communication, Autonomy: Principles and Technologies
PIs: Gaurav Sukhatme and Leana Golubchik

Institution: University of Southern California

REU Site: Coordination, Communication, Autonomy: Principles and Technologies

PI: Gaurav S. Sukhatme Institution: University of Southern California

The objective of this REU site is to train students in research methodology and to enthuse them about Computer Science research. The intended impact is that a signicant proportion of the students go on to graduate school. Students will work with faculty mentors and their graduate students at the Computer Science department at the University of Southern California. A training program in ethics is planned as are three external research visits to local research labs in the Los Angeles area. A vibrant social program is planned to facilitate a sense of community wherein students will interact with participants in summer internship programs on the USC campus. A formal assessment of outcomes is planned via a principled and rigorous site evaluation program. The participating faculty mentors are well established in their fields and span a spectrum of cutting edge research topics in Computer Science.

Students participating in the program will have a chance to work on projects spanning robotics, agents and artificial intelligence, networks and systems, algorithms and modeling, and software engineering. Within robotics, projects are available in the newly emerging societally relevant area of robotics for healthcare and at- risk populations, networked robotics for scientic discovery covering water and air pollution monitoring, and humanoid robotics. Agents and artificial intelligence projects span multi-agent systems, planning and learning. Networking and systems projects include networked sensing and its use in scientic applications, scalable and robust routing infrastructures in large networks such as the Internet, data dissemination in wireless sensor networks, structural properties of the Internet and high level abstractions and analytical models.
Within algorithms and modeling, students may work on graph algorithms, applications to networks, randomized algorithms, and information flow through networks. Finally, projects are available in software architecture modeling and analysis for embedded systems, middleware facilities for architectural implementation and software reliability modeling.

We are especially interesting in attracting participation in the USC Computer Science REU site from three populations: 1. academically talented students from traditionally underserved colleges and universities, 2. women, and 3. underrepresented minorities.

The objective of this research is the design of innovative routing,
planning and coordination strategies for robot networks, and their
application to oceanography. The approach is organized in three
synergistic thrusts: (1) the application of queueing theory and
combinatorial techniques to networked robots performing sequential tasks,
(2) the design of novel distributed optimization and coordination schemes
relying only on asynchronous and asymmetric communication, (3) the design
of practical routing and coordination algorithms for the USC Networked
Aquatic Platforms. In collaboration with oceanographers and marine
biologists, the project aims to design motion, communication and
interaction protocols that maximize the amount of scientific information
collected by the platforms.

This proposal addresses multi-dimensional problems of relevance in
Engineering and Computer Science by unifying fundamental concepts from
multiple cyberphysical domains (robotics, autonomy, combinatorics, and
network science). Our team has expertise in a broad range of scientific
disciplines, including control theory and theoretical computer science and
their applications to multi-agent systems, robotics and sensor networks.

The proposed research will have a positive impact on the emerging
technology of autonomous and reliable robotic networks, performing a broad
range of environmental monitoring and logistic tasks. Our educational and
outreach objectives are manifold and focus on (1) integrating the proposed
research themes into undergraduate education and research, e.g., via the
existing NSF REU site at the USC Computer Science Department, and (2)
mounting a vigorous program of outreach activities, e.g., via a
well-developed collaboration with the UCSB Center for Science and
Engineering Partnerships.

"This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5)."
Proposal #: 09-60163
PI(s): Sukhatme, Gaurav; Caron, David A.; Edwards, Katrina J.; Jones, Burton, H; Mitra, Urbashi
Institution: University of Southern California
Title: MRI-R2: Acq. of a Networked AUV-based Instrument for the Southern California Bight
Project Proposed:
This project, acquiring a networked instrument composed of two complementary Autonomous Underwater Vehicles (AUVs), supports an extensive program of research in robotics, underwater acoustic communication and networking, marine biology, oceanography, and biogeochemistry at the Center for Integrated Networked Aquatic PlatformS (CINAPS). These two AUVs add capability to those already in use by CINAPS. The work addresses two current key limitations: limited depth (depth no greater than 200m) and limited communication (only supports communication through the air, i.e., when the vehicles are not below the surface.) These instruments impact agendas in two main fields: research in the Southern California coastal marine ecosystem (in physical oceanography, geobiological oceanography, and microbial ecology), and research in robotics, communication, and networking.
The instrumentation consists of a Slocum Electric Glider and an EcoMapper-EP (Expandable Payload) Autonomous Underwater Vehicle. The Slocum glider, for use up to 1000m, was developed by in the early 1990's, and has become an integral tool of ocean science in this decade. It is driven entirely by a variable buoyancy system rather than active propulsion. The glider's wings convert the buoyancy-dependent vertical motion into forward velocity. Slocum gliders are truly autonomous, requiring a surface vessel only for deployment and recovery. On-board communications capabilities include a two-way RF modem, Iridium satellite and an ARGOS locator. With its onboard instrumentation, combined with its mobility and long-range capabilities, the glider provides continuous, near-real-time information, about the physics and biogeochemistry of the ocean. Designed specifically for water quality and bathymetry mapping applications, the Ecomapper for shallow use (up to 200m) is a unique AUV. Easily deployed by one person, it is able to perform a wide-area survey without a surface workboat or associated personnel. The EP class EcoMapper allows for additional ports for customized sensor integration and space for a second low-power CPU to support the additional sensors and software. This additional CPU enables mission adaptations to occur on-the-fly based on real-time sensor readings that are necessary when trying to detect and track dynamically changing oceanic features a range of important processes and associated questions can only be studied with the coupling of deep AUV operations (up to 1000m) with shallow (up to 200m). These include (a) the toxic effect of harmful algal blooms at greater depth, (b) the temporal and spatial variations of the low oxygen interface (deep basins can become hypoxic or anoxic due to isolation), and (c) fluxes of particulate material from urban runoff to the deeper sea that are discontinuous in both time and space; the spatial extent of which can be best resolved with autonomous vehicles that can maintain a presence over several events. To enable these studies, new algorithms for multi-AUV communication and control are necessary. The ability to coordinate vehicle trajectories and missions while submerged presents a current limitation. This is of particular concern in the Southern California Bight, the study site, a region with high maritime traffic where surfacing of the AUVs needs to be minimized. Used as a field instrument deployed in the Southern California Bight (SCB), the instrument is programmed and bench-tested in the Robotic Embedded Systems Laboratory on the USC main campus in LA. The instrument manager is supported by the PI;s grants. USC covers all operational and maintenance costs.
Broader Impacts:
The instrumentation impacts faculty research and student education, contributing to enable new science in diverse research projects and impacting the ongoing instruction at the institution and serves as a learning tool to develop student scientific proficiency (through existing courses and participation in faculty-led research). This acquisition plays an important role in the Southern California Bight Study planned for Spring 2010, coordinated by the Southern California Coastal Water Research Project (SCCWRP), a public agency focusing on the coastal ecosystems of Southern California. The PIs have close collaborative ties to this agency. Moreover, the networked and adaptive sensor systems provide important data relating to the climate and health of Southern California coastal ocean.
Undergraduate students are involved through REU site awards at USC. Since the PI runs the USC Computer Science department REU site for research in various areas of computer science, students in his lab often assist with data analysis and programming for the robotic boats. The EcoMapper vehicle, which is designed to be portable and deployable in shallow water, is particularly suitable for the next generation of REU students. Other PIs participate as faculty mentors in USC biochemistry REU site encouraging similar participation from undergraduate students in biology.

This project focuses on tackling a critical barrier to long-term autonomy for robotic systems, namely the lack of theoretically well-founded self-calibration methods for inertial and vision-based sensors, commonly found on sophisticated robots. The project is motivated by the vision of power-up-and-go robotic systems that are able to operate autonomously for long periods without requiring tedious manual sensor calibration. The research team addresses this problem in the context of vision-based mobile manipulation and navigation. The core foci of the work are: 1. the development of a unified mathematical theory of anytime, automatic calibration for visual-inertial systems, and 2. an experimental characterization of the resulting algorithms with state-of-the-art, sophisticated robots of significant diversity (humanoids performing mobile manipulation and autonomous ground vehicles navigating outdoors). Inertial sensing is critically important for humanoid balance control, while visual sensing relates the 3D world to the robot's body coordinates thereby enabling manipulation. In the case of autonomous ground vehicles, monocular and stereo camera calibration is still commonly performed manually using a known calibration target. The project obviates the need for this requirement. The expected outcomes of the project are: 1. a theoretical foundation for humanoid robots to function autonomously in unstructured environments over significant periods of time, and 2. new navigation algorithms for ground vehicles allowing them to see further with greater acuity. The project explicitly incorporates undergraduate research in cooperation with an REU site currently operational at the USC Computer Science Department.

To build high quality software, it is increasingly important for engineers to reason quantitatively about critical system properties, such as performance and reliability, as early as possible in the design process. Ideally, these properties are assessed before significant time and cost are expended on implementation. However, making useful predictions in early design stages is difficult at best due to the interplay among many relevant factors, such as complex properties of software components, effects of the firmware, and conflicts between desired system attributes. In this project, we focus on design-time evaluation of architectures with respect to one key attribute: reliability. The approach we pursue will enable an engineer to build a multi-faceted, hierarchical model of a system and assess its reliability in an incremental, scalable fashion. Moreover, we particularly focus on the area of embedded systems. Embedded systems present a rich target of opportunity for this work as (1) they demand close interplay of software and execution substrate; (2) they are often unencumbered with legacy concerns, allowing easier introduction and exploration of new techniques such as the one we propose; (3) they often have stringent and complex requirements, yet are seldom approached from a software architectural perspective. An extensive evaluation of our research focuses on two measures of interest: tractability (intended to address scalability issues existing in real, complex systems) and sensitivity (intended to address issues of confidence in our predictions under numerous design-time uncertainties).

CDI-Type I: Collaborative Research: Collaborative Multi-robot
Exploration of the Coastal Ocean (COMECO)

Overview: The coastal ocean is a complex environment driven by the interaction of atmospheric, oceanographic, estuarine/riverine, and land-sea processes, which result in dynamic coastal features such as blooms, anoxic zones, and plumes (estuarine, oil, pollutant). Effective observation and quantification of these features require simultaneous, rapid measurement of diverse water properties to capture its variability. This project aims to synthesize and understand the basic principles of environmental sensing based on the integration of adaptive robotic sampling with human decision-making. The techniques being developed augment existing ocean models and aid coastal exploration to ensure that robots are present at the "right place and time" to provide the most effective measurements.

Technical Description: The absence of a single model assimilating all available physical and biogeochemical data to provide a reliable view of ocean features favors the combination of human expertise, model refinement, and analytical adaptive sampling adopted in this project. Human decision-making is coupled with probabilistic modeling and learning in a decision support system enabling environmental field model discovery and refinement. The project extends the state of the art in multirobot adaptive sampling by investigating the relationship between environmental field structure and sampling performance, developing improved field boundary tracking techniques, and creating methods for multi-resolution, multivariable sampling. These advances are being made by addressing two broad research challenges. The first, Model-Based Asset Allocation, involves synthesis of large-scale, low-resolution data with human scientific expertise to make timely, model-informed asset allocation decisions. The second, Sampling-Based Model Refinement, involves small-scale, high-resolution autonomous cooperative selection and execution of robot sampling trajectories. Both challenges involve the handling of multivariate, multi-resolution, temporally evolving fields. The project includes a feasibility and evaluation study in coastal ocean exploration using underwater robots.

Broader Impacts: Decision support with diverse data integrated in a form that is interpretable by a non-computer specialist will have a broader impact applicable to a range of domains, including ocean and space exploration, environmental disaster response and military andhomeland security. The ocean science community will have a new and powerful tool to augment their understanding of dynamic coastal phenomena and policy makers an important tool to aid decision making impacting coastal communities. It is expected that the methods developed will be broadly applicable to the general task of goal-driven exploration and characterization of large areas. The project will involve graduate students who will be trained in an interdisciplinary context. The project results will be disseminated in the peer-reviewed scientific literature as well as via the project website at: http://robotics.usc.edu/comeco.html

Future scientific and technological efforts to achieve better understanding of oceans and water-related applications will rely heavily on our ability to jointly consider communications, actuation and sensing in a unified system that includes instruments, vehicles, human operators and sensors of all types. The goals of this project are to design networking tools for mobile underwater networks, develop novel navigation mechanisms for communication-constrained autonomous underwater vehicles and to ultimately integrate sensing and classification to provide solutions for the exploration-exploitation tradeoff.

This project will lead to development of underwater communication methods with applications to science, security, and industry in the areas of environmental monitoring, aquatic eco-system analysis, ocean accident remediation, surveillance for defense applications, homeland security, oil and gas, aquaculture, geological and oceanographic science, and marine biology. It will also contribute to the training of new information technology professionals and scientists with expertise in interdisciplinary research spanning underwater networks, oceanography and computer science.

Researchers aim to develop cost-effective, accurate system to get everyday people to be more active. This technology is a behavioral monitoring and analytics platform that uses the sensors on a smartphone to detect, characterize and quantify human movement irrespective of where the sensor is placed on a person. The team uses just-in-time nudging and "big-data" similarity metrics to induce behavioral changes and entice people to be more active. The mission of this project is to create a cellphone-based service to measure and aggregate parameters of personal activity and provide analytic tools for personal awareness. The team plans to further develop the user interface and user experience with designs that characterize, analyze and report daily movement in anticipation of increasing daily activity.

By providing an intelligent system that develops big-data models of daily activity and linking it to social circles, this technology has the potential to assist people in becoming more active. Widespread use of this technology could lead to the design and deployment of an ecosystem to monitor movement behavior in daily living conditions. The final version of this technology is designed to provide a cost-effective and accurate method to track caloric intake and expenditure in the palm of an individual's hand. From a healthcare perspective, quantifying and analyzing behaviors could help develop insight into the links between daily behaviors and lifestyle-based diseases.

This project envisions the future of scientific exploration as a collaborative endeavor between human scientists and autonomous robotic systems. The key challenge to materializing this vision lies in combining the expert knowledge of the scientist with the optimization capabilities of the autonomous system. The scientist brings specialized knowledge and experience to the table, while the autonomous system is capable of processing and evaluating large quantities of data. This research leverages these complementary strengths to develop a collaborative system capable of guiding scientific exploration and data collection by integrating input from scientists into an autonomous learning and planning framework. This is achieved by combining probabilistic planning with inverse reinforcement learning to integrate human input and prior knowledge into a unified optimization framework in the context of scientific exploration. The project team is validating the approach in the challenging domain of autonomous underwater ocean monitoring. This domain is particularly well suited for the testing of human-robot collaboration due to the limited communication available underwater and the necessary supervised autonomy capabilities. By integrating feedback from the human user into an algorithmic planning framework, the goal is to improve the efficiency of scientific data collection and gather data about phenomena that were previously outside the reach of scientific investigation. The use of autonomous vehicles for scientific data collection is becoming increasingly prominent; however, the research community lacks a foundational understanding of the interactions between scientists and autonomous vehicles. This work focuses on principled methods for integrating human input into algorithmic optimization techniques moving towards the goal of supervised autonomy for robots.

This project has the potential to change the way scientific data are collected through the development of a foundational framework for human-robot scientific collaboration. Such a framework is expected to have broad implications throughout the fields of human-robot interaction and artificial intelligence. The proposed research is being integrated into the robotics and computer science curriculum at both the graduate and undergraduate levels. It is also being utilized for K-12 robotics outreach programs in Los Angeles. The algorithms created in this research are transitioned to field tests and operations via ongoing collaborations with the Monterey Bay Aquarium Research Institute (MBARI) and the Southern California Coastal Ocean Observing System (SCCOOS).

This research investigates how robots can plan effectively in dynamic settings. The focus is on spatio-temporal ocean monitoring with autonomous underwater robots. Such monitoring and sensing allows scientists to gain a greater understanding of the planet and its environmental processes. For autonomous operation in the ocean, robots need to make decisions in spite of extensive uncertainty in ocean currents that change both spatially and temporally, and are often strong enough to alter the vehicle's motion significantly. The algorithms developed in this work are tested in the underwater domain with oceanographic phenomena of interest (e.g., algal blooms). Tests are performed on multiple datasets available at USC from prior field trials with underwater robots. Through the use of both robot testing and simulated testing, the methods developed in this work are validated across a wide range of applications and scales.

When planning for a long-range and long-term environmental monitoring task, an accurate planner requires a forecast of ocean currents at the destination and intermediate way-points in order to determine corresponding actions. A drawback of state-of-the-art decision making methodologies lies in the fact that they rely on a known uncertainty description that is a "snapshot at a certain moment in time." In this research, the focus is on the design of new mechanisms that generalize state-of-the-art decision-making methodologies to overcome this limitation. The goal is the creation of a general methodology to compute control policies that consider not only a fully known (current and past) stochastic description, but also a (possibly uncertain) prediction of future dynamics. The stochastic transition dynamics are considered as a noisy or uncertain function that varies with some parameter such as the time. To solve this problem, a new and efficient value propagation mechanism is developed involving two processes that evolve in both spatial and temporal dimensions.

This Research Experiences for Undergraduates (REU) site will train students in research methodology by engaging them in authentic research projects and using a focused approach to promote a sense of belonging in the research community. The participating faculty mentors span the spectrum of cutting-edge research topics in robotics and are well established in their fields. Students will participate in research projects that cross-cut multiple areas of robotics and autonomous systems, including socially assistive robotics, robot learning, networked robotics, and robotics data processing. They will gain research experience in solving some of the most challenging modern robotics and autonomy problems and get exposure to robotics research beyond the scope of the REU site, through seminars by other University of Southern California faculty and external site visits, to aid in planning their next career steps. The intended impact is to have a significant proportion of the REU students go on to graduate school.

The goal of this REU site is to incorporate into the professional research community a cohort of undergraduate student participants from three populations: (1) academically talented students from traditionally underserved colleges and universities, (2) women, and (3) underrepresented minorities. It will use expertise from the USC Viterbi School of Engineering's Center for Engineering Diversity to explicitly promote diversity in the REU student pool. The dual objective is to train carefully selected students in research methodology, and excite them about robotics research by providing a compelling cohort experience. Students will work with faculty mentors and their Ph.D. students, interact with an existing Viterbi School of Engineering Research Experiences for Teachers (RET) site, and engage in three relevant external research visits. A rigorous assessment of outcomes is planned. To facilitate the sense of a research cohort and encourage networks, housing will be provided in on-campus apartments, as will additional social programs.