Professor Rafael Fierro(University of New Mexico): Multi-Agent Coordination for On-Orbit Servicing and Satellite Life Extension

ammb23 Seminar, Upcoming Seminars

Professor Rafael Fierro

Professor, Departments of Electrical Engineering and Computer Engineering, University of Maryland

Time: 12:30 pm – 1:30 pm

Date: Friday, March 21

Location: BC 220

Abstract: This seminar presents our efforts to extend satellite lifespan using robotic technologies under the Breaking the Launch Once, Use Once Paradigm project. I will focus on Interactive Dislodging technologies developed at the University of New Mexico, including safe proximity operations, gaining custody of resident space objects (RSOs), dislodging jammed components, and upgrade/repair operations. On-orbit maintenance is challenging due to spacecraft fragility and high inertia, requiring precise handling. To address this, we employ adaptive control to manage uncertainties in client dynamics and stiffness variations. We have developed two dislodging methods: (1) a decentralized approach using multiple free-flying agents and (2) an extension of this approach to a multi-robot arm system. Additionally, I will present a robust adaptive MPC for the on-orbit dislodging of a jammed solar panel. We validate our approach through zero-gravity simulations, demonstrating improved performance over state-of-the-art control schemes. I will describe our recent work on emulating stiction effects and verifying our algorithms on a state-of-the-art dual-robot gantry for an on-orbit space simulator testbed.

Bio: Rafael Fierro is a Professor in the Department of Electrical and Computer Engineering at the University of New Mexico, a position he has held since 2007. He earned an MSc in control engineering from the University of Bradford, England (1990), and a Ph.D. in electrical engineering from the University of Texas at Arlington (1997). Before joining UNM, he was a postdoctoral researcher at the GRASP Lab at the University of Pennsylvania and later a faculty member at Oklahoma State University.  Dr. Fierro’s research focuses on cyber-physical systems, robotic networks, multi-agent coordination, UAVs, and collaborative robot manipulation for on-orbit servicing. His work has been supported by the NSF, US Army Research Laboratory (ARL), Air Force Research Laboratory (AFRL), DOE, Sandia National Laboratories, and the Breakthrough Foundation. He directs the AFRL-UNM Agile Manufacturing Center and the MARHES Lab.  A recipient of a Fulbright Scholarship, an NSF CAREER Award, and the 2008 ISA Transactions Best Paper Award, Dr. Fierro has also served as an associate editor for several IEEE journals.

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Professor Robin Murphy(Texas A&M University): Being There: 30 Years of Disaster Robotics

ammb23 Seminar

Professor Robin Murphy

Professor, Department of  Computer Science & Engineering, Texas A&M University

Time: 12:30 pm – 1:30 pm

Date: Friday, March 7

Location: BC 220

Abstract: This talk will review 30 years of disaster robotics, tracing its start from the 1995 Kobe Earthquake and the Oklahoma City bombing to the current state of the art.  Since 2001, disaster robotics have made a positive contribution to both research and society. Mini ground robots were first used for the immediate response phase of the 9/11 World Trade Center collapse, small unmanned aerial systems for assessing damage and needs of civilians have become common since Hurricane Harvey, and marine vehicles that routinely assist lifeguards on beaches have helped with mass casualty events such as the Syrian Refugee Crisis in Greece. Perhaps more surprisingly, robots of all types were readily adopted by civilians, not just governments, during the COVID pandemic. Most recently, exciting breakthroughs in computer vision and machine learning are enabling responders to rapidly make more informed decisions to better save lives and accelerate recovery. Based on personal involvement with over 30 disasters and analysis of the use of robotics in dozens more, three summative observations about AI and robotics have emerged. First, and foremost, disaster robotics remains a formative domain better suited for qualitative and field methodologies than traditional hypothesis-driven laboratory studies. Second, understanding the unique socio-technical attributes of the work domain is vital in identifying high-impact fundamental research topics. Third, disasters pose ethical challenges for responsible research and innovation. The talk concludes with personal suggestions for those interested in careers in research. 

Bio: Dr. Robin R. Murphy, Ph.D. (’92) and M.S. (‘89) in computer science and B.M.E. (‘80) from the Georgia Institute of Technology,  is the Raytheon Professor of Computer Science and Engineering at Texas A&M University and a director of the Center for Robot-Assisted Search and Rescue. Her research focuses on artificial intelligence, robotics, and human-robot interaction for emergency management. She is an AAAS, ACM, and IEEE Fellow, a TED speaker, and the author of over 400 papers and four books including the award-winning Disaster Robotics which captures her research deploying ground, aerial, and marine robots to over 30 disasters in five countries including the 9/11 World Trade Center, Fukushima, Hurricane Ian, and the Surfside collapse. Her contributions to robotics have been recognized with the ACM Eugene L. Lawler Award for Humanitarian Contributions and a US Air Force Exemplary Civilian Service Award medal.  Dr. Murphy has served on numerous professional and government boards, including the Defense Science Board and National Science Foundation, as well as the AI for the Benefit of Humanity prize committee.

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Professor Calin Belta(University of Maryland): Formal Methods for Safety-Critical Control using Control Barrier Functions

ammb23 Seminar

Professor Calin Belta

Professor, Departments of Electrical Engineering and Computer Engineering, University of Maryland

Time: 12:30 pm – 1:30 pm

Date: Friday, February 21th

Location: BC 220

Abstract: In control theory, complicated dynamics such as systems of (nonlinear) differential equations are mostly controlled to achieve stability and to optimize a cost. In formal synthesis, simple systems such as finite state transition graphs modeling computer programs or digital circuits are controlled from specifications such as safety, liveness, or richer requirements expressed as formulas of temporal logic. With the development and integration of cyber-physical and safety-critical systems, there is an increasing need for computational tools for controlling complex systems from rich, temporal logic specifications, while ensuring safety. Recent works proposed computational efficient approaches for safety-critical control using Control Barrier Functions (CBF) and Control Lyapunov Functions (CLF). In this talk, I will show how these approaches can be extended to accommodate systems with high relative degrees, (partially) unknown dynamics, and temporal logic specifications, and to improve the feasibility of the associated optimization problems.

Bio: Calin Belta is the Brendan Iribe Endowed  Professor of  Electrical and Computer Engineering and Computer Science at the University of Maryland, College Park, which is also part of the Maryland Robotics Center (MRC) and the Institute for Systems Research (ISR).  His research focuses on making control and machine learning systems safe and Interpretable,  with particular emphasis on robotics and systems biology. Notable awards include the 2008 AFOSR YIP and the 2005 NSF CAREER. He is a Fellow of the IEEE.

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Abhinav Verma(Penn State University): Safe and Performant Policies via Specification Guided Reinforcement Learning

ammb23 Seminar

Dr. Abhinav Verma

Assistant Professor, Departments of Electrical Engineering and Computer Science, Penn State University

Time: 12:30 pm – 1:30 pm

Date: Friday, January 31st

Location: BC 220

Abstract: Specifications in linear temporal logic (LTL) offer a simplified way of specifying tasks for policy optimization that may otherwise be difficult to describe with scalar reward functions. However, the standard Reinforcement Learning (RL) frameworks can be too myopic to find maximally satisfying policies. In this talk we will discuss eventual discounting, a value-function based proxy under which one can find policies that satisfy a specification with the highest achievable probability. To improve the efficiency of learning from specifications we combine eventual discounting with LTL-guided Counterfactual Experience Replay, a method for generating off-policy data from on-policy rollouts via counterfactual reasoning. Finally, we will discuss a mechanism for exploiting the compositionality of a LTL specification to provide formal guarantees on the behavior of learnt policies for reach-avoid tasks.

Bio: Dr. Verma Is an Assistant Professor in the Department of Computer Science and Engineering at The Pennsylvania State University. Previously, He was a postdoc at the Institute of Science and Technology (IST) Austria in the Henzinger Group. Before joining IST, He completed his PhD from the University of Texas at Austin advised by Prof. Swarat Chaudhuri. His research lies at the intersection of machine learning and formal methods, with a focus on building intelligent systems that are reliable, transparent, and secure. This work builds connections between the symbolic reasoning and inductive learning paradigms of artificial intelligence.

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