Enabling Human-Aware Automation: A Dynamical Systems Perspective on Human Cognition

November 13, 2020 – 11:00 AM
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Neera Jain

Perdue University

Abstract

Across many sectors, ranging from manufacturing to healthcare to the military theater, there is growing interest in the potential impact of automation that is truly collaborative with humans. Realizing this impact, though, rests on first addressing the fundamental challenge of designing automation to be aware of, and responsive to, the human with whom it is interacting. While a significant body of work exists in intent inference based on human motion, a human’s physical actions alone are not necessarily a predictor of their decision-making. Indeed, cognitive factors, such as trust and workload, play a substantial role in their decision making as it relates to interactions with autonomous systems. In this talk, I will describe our interdisciplinary efforts at tackling this problem, focusing on recent work in which we synthesized a near-optimal control policy using a trust-workload POMDP (partially-observable Markov decision process) model framework that captures changes in human trust and workload for a context involving interactions between a human and an intelligent decision-aid system. Using automation transparency as the feedback variable, we designed a policy to balance competing performance objectives in a reconnaissance mission study in which a virtual robotic assistant aids human subjects in surveying buildings for physical threats. I will present experimental validation of our control algorithm through human subject studies and highlight how our approach is able to mitigate the negative consequences of “over trust” which can occur in such interactions. I will also briefly discuss our related work involving the use of psychophysiological data and classification techniques as an alternative method toward real-time trust estimation.

Biography

Dr. Neera Jain is an Assistant Professor in the School of Mechanical Engineering and a faculty member in the Ray W. Herrick Laboratories at Purdue University. She directs the Jain Research Laboratory with the aim of advancing technologies that will have a lasting impact on society through a systems-based approach, grounded in dynamic modeling and control theory. A major thrust of her research is the design of human-aware automation through control-oriented modeling of human cognition. A second major research thrust is optimal design and control of complex energy systems. Dr. Jain earned her M.S. and Ph.D. degrees in mechanical engineering from the University of Illinois at Urbana-Champaign in 2009 and 2013, respectively. She earned her S.B. from the Massachusetts Institute of Technology in 2006. Upon completing her Ph.D., Dr. Jain was a visiting member of the research staff at Mitsubishi Electric Research Laboratories where she designed model predictive control algorithms for HVAC systems. In 2015 she was a visiting summer researcher at the Air Force Research Laboratory at Wright-Patterson Air Force Base. Dr. Jain and her research have been featured in NPR and Axios. As a contributor for Forbes.com, she writes on the topic of human interaction with automation and its importance in society. Her research has been supported by the National Science Foundation, Air Force Research Laboratory, Office of Naval Research, as well as private industry.

Three Problems in Mathematical Oncology

October 16, 2020 – 03:15 PM
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Paul K. Newton

University of Southern California

Abstract

I will introduce three problems in mathematical oncology all of which involve nonlinear dynamics and control theory. First, I will describe our work using Markov chain models to forecast metastatic progression. The models treat progression as a (weighted) random walk on a directed graph whose nodes are tumor locations, with transition probabilities obtained through historical autopsy date (untreated progression) and longitudinal data (treated) from Memorial Sloan Kettering and MD Anderson Cancer Centers. Then, I will describe our models that use evolutionary game theory (replicator dynamics with prisoner’s dilemma payoff matrix) to design multi-drug adaptive chemotherapy schedules to mitigate chemo-resistance by suppressing ‘competitive release’ of resistant cell populations. The models highlight the advantages of antagonistic drug interactions (over synergistic ones) in shaping the fitness landscape of co-evolving populations. Finally, I will describe our work on developing optimal control schedules (based on Pontryagin’s maximum principle) that maximize cooperation for prisoner’s dilemma replicator dynamical systems. As much as possible with the Zoom format, I hope the seminar will be interactive and a starting point for further discussions.

Biography

Professor Newton received his B.S. (cum laude) degree in Applied Mathematics/Physics at Harvard University in 1981 and his Ph.D. in 1986 from the Division of Applied Mathematics at Brown University. He then moved to the Mathematics Department at Stanford University to work as a post-doctoral scholar under J.B. Keller. He became Assistant (1987) and Associate Professor (1993) in the Mathematics Department at the University of Illinois Champaign-Urbana (UIUC) and at the Center for Complex Systems Research (CCSR) at the Beckman Institute. In 1993 he moved to the Aerospace & Mechanical Engineering Department and the Mathematics Department at the University of Southern California and was promoted to Full Professor in 1998. Trained as an applied mathematician, Professor Newton’s work focuses on developing mathematical models for nonlinear dynamical processes in continuum mechanics and biophysics, currently focusing mostly on mathematical oncology and systems biology. He has held visiting appointments at Caltech, Brown, Hokkaido University, The Kavli Institue for Theoretical Physics at UC Santa Barbara, and The Scripps Research Institute where he functioned as head of the mathematical modeling section of the NCI supported Physical Sciences Oncology Center (2009-2014). He is currently a Professor of Applied Mathematics, Engineering, and Medicine in the Viterbi School of Engineering, the Dornsife College of Letters, Arts and Sciences, the Norris Comprehensive Cancer Center in the Keck School of Medicine, and a founding affiliate member of the LJ Ellison Institute for Transformative Medicine of USC. He currently serves as Editor-in-Chief of the Journal of Nonlinear Science (SpringerNature).

A Decoupling Principle in Stochastic Optimal Control and Its Implications

February 28, 2020 – 11:15 AM
TSRB auditorium

Suman Chakravorty

Texas A&M University

Abstract

The problem of Stochastic Optimal Control is ubiquitous in Robotics and Control since it is the fundamental formulation for decision-making under uncertainty. The answer to the problem can be computed by solving an associated Dynamic Programming (DP) problem. Unfortunately, the DP paradigm is also synonymous with the infamous “Curse of Dimensionality (COD),” a phrase coined by the discoverer of the Dynamic Programming paradigm, Richard Bellman, nearly 60 years ago, to capture the fact that the computational complexity of solving a DP problem grows exponentially in the dimension of the state space of the problem.

In this talk, we will introduce a newly discovered paradigm in stochastic optimal control, called “Decoupling,” that allows us to separate the design of the open and closed loops of a stochastic optimal control problem with continuous control space. This Decoupled solution allows us to break the COD inherent in DP problems, while remaining near-optimal, to third order, to the true stochastic control. The implications of the Decoupled design are examined in the context of Model Predictive Control (MPC) and Reinforcement Learning (RL). We shall introduce two algorithms, called the Trajectory Optimized Perturbation Feedback Control (T-PFC), and the Decoupled Data based Control(D2C), for the MPC and RL problems respectively. We shall also examine the consequences of the decoupling principle in partially observed/ belief space planning problems and present the Trajectory optimized Linear Quadratic Gaussian (T-LQG) algorithm.

Biography

Suman Chakravorty obtained his B.Tech in Mechanical Engineering in 1997 from the Indian Institute of Technology, Madras and his PhD in Aerospace Engineering from the University of Michigan, Ann Arbor in 2004. From August 2004- August 2010, he was an Assistant Professor with the Aerospace Engineering Department at Texas A&M University, College Station and since August 2010, he has been an Associate Professor in the department. Dr. Chakravorty’s broad research interests lie in the estimation and control of stochastic dynamical systems with application to autonomous, distributed robotic mapping and planning, and situational awareness problems. He is a member of AIAA, ASME and IEEE. He is an Associate Editor for the ASME Journal on Dynamical Systems, Measurement and Control and the IEEE Robotics and Automation Letters.

On strategic information transmission in Cyber-Socio-Physical Systems

February 14, 2020 – 11:15 AM
TSRB auditorium

Cedric Langbort

University of Illinois at Urbana Champaign

Abstract

We consider situations in which better informed agents must decide which message to transmit to a decision-making receiver, so as to influence the decision in their favor.

Such scenarios, some versions of which have been considered earlier in Economics under the umbrellas of ‘cheap talk’ and ‘persuasion theory’ have found renewed relevance in a number of cyber-socio-physical contexts, and have interesting connections to both information and game theory.

Starting with the simplest single transmitter-single receiver setup, we present several variants of such strategic information transmission of increasing complexity, in terms of (1) strategic refinement and rationality of the players, (2) information asymmetries and (3) sender network structure, as well as applications.

The nature of equilibrium sending strategies, as well as the sender’s ability to reach an appropriate decision despite deception vary widely depending on these assumptions, thus illustrating the subtlety of these deception games.

Biography

Cedric Langbort is an Associate Professor of Aerospace Engineering at the University of Illinois at Urbana–Champaign (UIUC), where he is also affiliated with the Decision & Control Group at the Coordinated Science Lab (CSL), and the Information Trust Institute. Prior to joining UIUC in 2006, he studied at the Ecole Nationale Superieure de l’Aeronautique et de l’Espace-Supaero in Toulouse (France), the Institut Non-Lineaire in Nice (France), and Cornell University, from which he received the Ph.D. in Theoretical & Applied Mechanics in January 2005. He also spent a year and a half as a postdoctoral scholar in the Center for the Mathematics of Information at Caltech. He works on applications of control, game, and optimization theory to a variety of fields; most recently to “smart infrastructures” problems within the Center for People & Infrastructures which he co-founded and co-directs at CSL. He is a recipient of the NSF CAREER Award, the advisor of an IEEE CDC Best Student Paper Award recipient, and has been an associate editor for OCAM, the journal of Optimal Control Application and Methods, as well as for Systems & Control Letters.

Universal approximation of input-output maps by temporal convolutional nets

November 22, 2019 – 11:15 AM
TSRB Auditorium

Max Raginsky

University of Illinois at Urbana Champaign

Abstract

A number of problems in machine learning involve sequence-to-sequence transformations, i.e., nonlinear operators that map an input sequence to an output sequence. Traditionally, such input-output maps have been modeled using discrete-time recurrent neural nets. However, there has been a recent shift in sequence-to-sequence modeling from recurrent network architectures to autoregressive convolutional network architectures. These temporal convolutional nets (TCNs) allow for easily parallelizable training and operation, while still achieving competitive performance. In this talk, based on joint work with Joshua Hanson, I will show that TCNs are universal approximators for a large class of causal and time-invariant input-output maps that have approximately finite memory. Specifically, I will present quantitative approximation rates for deep TCNs with rectified linear unit (ReLU) activation functions in terms of the width and depth of the network and the modulus of continuity of the original input-output map. Next, I will show how to apply these results to input-output maps with incrementally stable nonlinear state-space realizations. As an example, I will discuss a class of nonlinear systems of Lur’e type that satisfy a variant of the discrete-time circle criterion.

Biography

Maxim Raginsky received the B.S. and M.S. degrees in 2000 and the Ph.D. degree in 2002 from Northwestern University, all in Electrical Engineering. He has held research positions with Northwestern, the University of Illinois at Urbana-Champaign (where he was a Beckman Foundation Fellow from 2004 to 2007), and Duke University. In 2012, he has returned to the UIUC, where he is currently an Associate Professor with the Department of Electrical and Computer Engineering and the Coordinated Science Laboratory. He also holds a courtesy appointment with the Department of Computer Science.

Formal Verification of End-to-End Deep Reinforcement Learning

November 08, 2019 – 11:15 AM
TSRB Auditorium

Yasser Shoukry

University of California, Irvine

Abstract

From simple logical constructs to complex deep neural network models, Artificial Intelligence (AI)-agents are increasingly controlling physical/mechanical systems. Self-driving cars, drones, and smart cities are just examples of such systems to name a few. However, regardless of the explosion in the use of AI within a multitude of cyber-physical systems (CPS) domains, the safety, and reliability of these AI-enabled CPS is still an understudied problem. Mathematically based techniques for the specification, development, and verification of software and hardware systems, also known as formal methods, hold the promise to provide appropriate rigorous analysis of the reliability and safety of AI-enabled CPS. In this talk, I will discuss our work on applying formal verification techniques to provide formal verification of the safety of autonomous vehicles controlled by end-to-end machine learning models and the synthesis of certifiable end-to-end neural network architectures.

Biography

Yasser Shoukry is an Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of California, Irvine where he leads the Resilient Cyber-Physical Systems Lab. Before joining UCI, he spent two years as an assistant professor at the University of Maryland, College Park. He received his Ph.D. in Electrical Engineering from the University of California, Los Angeles in 2015. Between September 2015 and July 2017, Yasser was a joint postdoctoral researcher at UC Berkeley, UCLA, and UPenn. His current research focuses on the design and implementation of resilient cyber-physical systems and IoT. His work in this domain was recognized by the NSF CAREER Award, the Best Demo Award from the International Conference on Information Processing in Sensor Networks (IPSN) in 2017, the Best Paper Award from the International Conference on Cyber-Physical Systems (ICCPS) in 2016, and the Distinguished Dissertation Award from UCLA EE department in 2016. In 2015, he led the UCLA/Caltech/CMU team to win the NSF Early Career Investigators (NSF-ECI) research challenge. His team represented the NSF- ECI in the NIST Global Cities Technology Challenge, an initiative designed to advance the deployment of Internet of Things (IoT) technologies within a smart city. He is also the recipient of the 2019 George Corcoran Memorial Award for his contributions to teaching and educational leadership in the field of CPS and IoT.

How I Learned to Stop Worry and Start Loving Lifting to Infinite Dimensions

November 01, 2019 – 11:15 AM
TSRB Auditorium

Ram Vasudevan

University of Michigan

Abstract

Autonomous systems offer the promise of providing greater safety and access. However, this positive impact will only be achieved if the underlying algorithms that control such systems can be certified to behave robustly. This talk will describe a pair of techniques grounded in infinite dimensional optimization to address this challenge. The first technique, which is called Reachability-based Trajectory Design, constructs a parameterized representation of the forward reachable set, which it then uses in concert with predictions to enable real-time, certified, collision checking. This approach, which is guaranteed to generate not-at-fault behavior, is demonstrated across a variety of different real-world platforms. The second technique, is a polynomial optimization method that allows one to compute globally optimal solutions in real-time in the presence of hundreds of constraints. The utility of this approach is validated on a real-time trajectory design task for an autonomous ground vehicle.

Biography

Ram Vasudevan is an assistant professor in Mechanical Engineering and the Robotics Institute at the University of Michigan. He received a BS in Electrical Engineering and Computer Sciences, an MS degree in Electrical Engineering, and a PhD in Electrical Engineering all from the University of California, Berkeley. He is a recipient of the NSF CAREER Award and the ONR Young Investigator Award. His work has received best paper awards at the IEEE Conference on Robotics and Automation, the ASME Dynamics Systems and Controls Conference, and IEEE OCEANS Conference.

Monitoring Over the Long Term and Rethinking Interaction in Multi-Robot Teams

October 25, 2019 – 11:15 AM
TSRB Auditorium

Ryan Williams

Virginia Tech

Abstract

In the area of multi-robot systems, an understanding of collaboration has been developed and is showing great promise in real-world applications including self-driving cars, industrial automation, precision agriculture, and disaster response. However, existing multi-robot methods still leave much to be desired. In this talk, I will argue for innovation in several areas of multi-robot coordination that are often seen in real-world applications: (1) intermittent monitoring of slowly-evolving spatiotemporal processes on large scales; (2) robots that interact asymmetrically due to imperfect sensors and interference; (3) robots that collaborate as an informed response to a team objective and the environment; and (4) robots that interact with human experts. Towards this vision, I will first discuss methods we have developed that generate temporal deployment plans for multi-robot teams that balance the cost of deployment with the quality of information gathered about some evolving process of interest. In particular, I will outline a combinatorial optimization approach to this “intermittent deployment” problem, yielding greedy solutions with bounded suboptimality. Next, I will detail recent results in coordinated motion control for robots interacting with sensors exhibiting some limited field of view (FOV), along with experimental results in outdoor environments. Finally, I will outline planning problems we have developed that generate temporal interaction structures for multi-robot teams along with trajectories that complement the efforts of humans experts in a search and rescue domain. Throughout the talk I will show examples of the projects at the Coordination at Scale Lab (CAS Lab) at Virginia Tech that motivate the above methods, and conclude by discussing future directions in the area of multi-robot systems.

Biography

Ryan K. Williams received the B.S. degree in computer engineering from Virginia Polytechnic Institute and State University in 2005, and the Ph.D. degree from the University of Southern California in 2014. He is currently an Assistant Professor in the Electrical and Computer Engineering Department at Virginia Tech where he runs the Laboratory for Coordination at Scale (CAS Lab). His current research interests include control, cooperation, and intelligence in distributed multi-agent systems, topological methods in cooperative phenomena, and distributed algorithms for optimization, estimation, inference, and learning. Williams is a Viterbi Fellowship recipient, has been awarded the NSF CISE Research Initiation Initiative grant for young investigators, is a Junior Faculty Award Recipient at Virginia Tech, is a best multi-robot paper finalist at the 2017 IEEE International Conference on Robotics and Automation, and has been featured by various news outlets, including the L.A. Times.

Low Gain Feedback: For Constrained Control, Nonlinear Stabilization and Control of Time Delay Systems

October 11, 2019 – 11:15 AM
TSRB 523A

Zongli Lin

University of Virginia

Abstract

Low gain feedback refers to a family of stabilizing state feedback gains that are parameterized in a scalar, referred to low gain parameter, and go to zero as the low gain parameter decreases to zero. Low gain feedback was initially proposed to achieve semi- global stabilization of linear systems subject to input saturation, and later found its other applications in the stabilization of nonlinear systems and linear systems with input delays. In this talk, we discuss the concept of low gain feedback, its properties, its design methods and its applications in constrained control, nonlinear stabilization and control of time-delay systems.

Biography

Zongli Lin is the Ferman W. Perry Professor in the School of Engineering and Applied Science and a Professor of Electrical and Computer Engineering at University of Virginia. He received his B.S. degree in mathematics and computer science from Xiamen University, Xiamen, China, in 1983, his Master of Engineering degree in automatic control from Chinese Academy of Space Technology, Beijing, China, in 1989, and his Ph.D. degree in electrical and computer engineering from Washington State University, Pullman, Washington, in 1994. His current research interests include nonlinear control, robust control, time delay systems, and control applications. He was an Associate Editor of the IEEE Transactions on Automatic Control (2001-2003), IEEE/ASME Transactions on Mechatronics (2006-2009) and IEEE Control Systems Magazine (2005-2012). He was elected a member of the Board of Governors of the IEEE Control Systems Society (2008- 2010, 2019-2021) and chaired the IEEE Control Systems Society Technical Committee on Nonlinear Systems and Control (2013-2015). He has served on the operating committees several conferences and was the program chair of the 2018 American Control Conference and a general chair of the 13th and 16th International Symposium on Magnetic Bearings (2012, 2018). He currently serves on the editorial boards of several journals and book series, including Automatica, Systems & Control Letters, Science China Information Sciences, and Springer/Birkhauser book series Control Engineering. He is a Fellow of IEEE, IFAC, and AAAS, the American Association for the Advancement of Science.

Distributed Energy Resources: PDEs and Hopfield Methods

October 04, 2019 – 11:15 AM
TSRB Auditorium

Scott Moura

UC Berkeley

Abstract

Variable renewable energy integration and resilience to extreme events motivate the need for flexible resources in electric power systems. Distributed energy resources (DERs), such as electric vehicles and thermostatically controlled loads, provide an intriguing set of distributed assets to provide flexible services in power systems. However, leveraging populations of DERs are challenging because they are (i) large-scale, and (ii) involve discrete-valued control. This talk addresses modeling, estimation, and control for aggregations of DERs. Specifically, the talk is divided into two parts. First, we discuss a partial differential equation (PDE) approach to modeling and estimating aggregations of DERs. Second, we discuss a novel class of methods for controlling DER populations that are mathematically formulated as large-scale mixed integer programs. We call this class of methods “Hopfield methods”.

Biography

Scott Moura is an Associate Professor in Civil & Environmental Engineering and Director of the Energy, Controls, & Applications Lab (eCAL) at the University of California, Berkeley. He is also a faculty member at the Tsinghua-Berkeley Shenzhen Institute. He received the B.S. degree from the University of California, Berkeley, CA, USA, and the M.S. and Ph.D. degrees from the University of Michigan, Ann Arbor, in 2006, 2008, and 2011, respectively, all in mechanical engineering. From 2011 to 2013, he was a Post-Doctoral Fellow at the Cymer Center for Control Systems and Dynamics, University of California, San Diego. In 2013, he was a Visiting Researcher at the Centre Automatique et Systèmes, MINES ParisTech, Paris, France. His research interests include control, optimization, and machine learning for batteries, electrified vehicles, and distributed energy resources.

Dr. Moura is a recipient of the National Science Foundation (NSF) CAREER Award, Carol D. Soc Distinguished Graduate Student Mentor Award, the Hellman Fellowship, the O. Hugo Shuck Best Paper Award, the ACC Best Student Paper Award (as advisor), the ACC and ASME Dynamic Systems and Control Conference Best Student Paper Finalist (as student and advisor), the UC Presidential Postdoctoral Fellowship, the NSF Graduate Research Fellowship, the University of Michigan Distinguished ProQuest Dissertation Honorable Mention, the University of Michigan Rackham Merit Fellowship, and the College of Engineering Distinguished Leadership Award.