Contributed by: Kadriye Merve Dogan, dogank@erau.edu
Spring 2021 FoRCE Online Seminars by Drs. Sofge, Chowdhary, Del Vecchio, Gregory, and Casbeer
We are pleased to announce the following Spring 2021 online seminars at FoRCE (http://force.eng.usf.edu/):
Seminar 1. March 12 (12:00p Eastern Time): Swarm Robotics Research at the U.S. Naval Research Laboratoryby Dr. Donald Sofge (U.S. Naval Research Laboratory)
Seminar 2. April 9 (12:00p Eastern Time): Deep Learning and Adaptive Control by Dr. Girish Chowdhary (University of Illinois at Urbana-Champaign)
Seminar 3. April 23 (12:00p Eastern Time): Control Theory for Engineering Biology by Dr. Domitilla Del Vecchio (Massachusetts Institute of Technology)
Seminar 4. May 14 (12:00p Eastern Time): Urban Air Mobility: A Control-Centric Approach to Addressing Technical Challenge by Dr. Irene M. Gregory (National Aeronautics and Space Administration)
Seminar 5. May 28 (12:00p Eastern Time): Control and Optimization as a Foundation for Multi-UAV Coordination by Dr. David Casbeer (Air Force Research Laboratory)
WebEx links for each talk as well as seminar abstracts and biographies are given at the bottom of this email.
We cordially hope that you will enjoy these seminars!
K. Merve Dogan, Embry-Riddle Aeronautical University, dogank@erau.edu
Tansel Yucelen, University of South Florida, yucelen@usf.edu
Seminar 1: Swarm Robotics Research at the U.S. Naval Research Laboratory (Dr. Donald Sofge)
WebEx Link: https://force.my.webex.com/force.my/j.php?MTID=m247f8b749f22b9e7d55bd782a3922618
Meeting number (access code): 182 758 5700
Meeting password: iJQ7KHXf5N5 (45775493 from phones and video systems)
Abstract: Swarm robotics, a subfield of both robotics and artificial swarm intelligence, focuses on the development of teams composed of large numbers of autonomous robotic agents. Like swarm intelligence, swarm robotics arises from the study of the phenomenology of biological systems in which large numbers of individuals collaborate in joint collective actions for the benefit of the community as a whole. However,
whereas swarm intelligence often utilizes the means and mechanisms of bio-inspired swarms for numerical optimization, the goals of bio-inspired robot swarms are generally concerned with the use of large numbers of low-cost physically embodied agents, acting together in a real-world environment, to achieve a common purpose. This talk will discuss key methods and bio-inspired algorithms for use in programming and controlling robotic swarms, and potential applications of these swarms.
Biography: Don Sofge is a Roboticist at the U.S. Naval Research Laboratory (NRL) with 31 years of experience in Artificial Intelligence and Control Systems R&D. He leads the Distributed Autonomous Systems Group in the Navy Center for Applied Research in Artificial Intelligence (NCARAI), where he develops nature-inspired computing paradigms to challenging problems in sensing, artificial intelligence, and control of autonomous robotic systems. His current research focuses on control of autonomous teams or swarms of robotic systems for Navy relevant missions. He has served as PI on dozens of federally funded R&D programs, and has more than 150 peer-reviewed publications on autonomy, intelligent control, quantum computing, and related topics. He has served as an advisor on autonomous systems to DARPA, ONR, OSD, ARL, NSF, and NASA, as well as US representative on international TTCP and NATO technical panels on autonomous systems, and has participated as a member of the National Science and Technology Council (NSTC) Networking and Information Technology Research and Development (NITRD) Program Interagency Working Groups: Intelligent Robotics and Autonomous Systems (IRAS), Machine Learning and Artificial Intelligence (MLAI), and the AI R&D Ad Hoc Group. Don also occasionally serves as an Adjunct Faculty Member at the University of Maryland where teaches graduate-level courses in Robotics.
Seminar 2: Deep Learning and Adaptive Control (Dr. Girish Chowdhary)
WebEx Link: https://force.my.webex.com/force.my/j.php?MTID=mcb3641e862639f345b1a28fe9a23620b
Meeting number (access code): 182 131 0237
Meeting password: Pyb93tavHW5 (79293828 from phones and video systems)
Abstract: Recent results in deep learning have left no doubt that it is amongst the most powerful modeling tools that we possess. The real question is how can we utilize deep learning for control without losing stability and performance guarantees. Even though recent successes in deep reinforcement learning (DRL) have shown that deep learning can be a powerful value function approximator, several key questions must be answered before deep learning enables a new frontier in robotics. DRL methods have proven difficult to apply on real-world robotic systems where stability matters and safety is critical. In this talk, I will present our recent work in bringing deep learning based methods to provably stable adaptive control and expand upon possibilities of using concepts from adaptive control to create safe and stable reinforcement learning algorithms. I will put our theoretical work in context by discussing several applications in flight control and agricultural robotics. I will also bring to light our recent work in understanding how the octopus brain works and how it can inspire future learning and distributed control tools.
Biography: Girish Chowdhary is an associate professor and Donald Biggar Willet Faculty Fellow at the University of Illinois at Urbana-Champaign. He is the director of the Field Robotics Engineering and Science Hub (FRESH) at UIUC and the Chief Scientist on the Illinois Autonomous Farm. Girish holds a joint appointment with Agricultural and Biological Engineering and Computer Science, he is a member of the UIUC Coordinated Science Lab, and holds affiliate appointments in Aerospace Engineering and Electrical Engineering. He holds a PhD (2010) from Georgia Institute of Technology in Aerospace Engineering, was a postdoc at the Laboratory for Information and Decision Systems (LIDS) of the Massachusetts Institute of Technology (2011-2013), and an assistant professor at Oklahoma State University (2013-2016). He also worked with the German Aerospace Center’s (DLR’s) Institute of Flight Systemsfor around three years (2003-2006). Girish’s work on AI and adaptive flight control has led to several key advances to flight-control and a Dave Ward memorial award by Aerospace Guidance and Controls committee. Girish is the author of over 100 publications in adaptive control, autonomy, and robotics, and PI on NSF, AFOSR, NASA, ARPAE, and DOE grants, and an ONR MURI. He is the winner of the Air Force Young Investigator Award, and several best paper awards, including a best systems paper award at RSS 2018 for his recent work on the agricultural robot TerraSentia. He is the co-founder of EarthSense Inc. (www.earthsense.co), working towards making sustainable farming profitable with ultralight field robots.
Seminar 3: Control Theory for Engineering Biology (Dr. Domitilla Del Vecchio)
WebEx Link: https://force.my.webex.com/force.my/j.php?MTID=m077fd39532be8e6d721d7828e4d08ed6
Meeting number (access code): 182 818 8385
Meeting password: QiqP6VceS57 (74776823 from phones and video systems)
Abstract: Genetic circuits control every aspect of life and thus the ability to engineer them de-novo opens exciting possibilities, from revolutionary drugs and green energy, to bugs that recognize and kill cancer cells. The robustness of natural gene networks is the result of million years of evolution and is in contrast with the fragility of today’s engineered circuits. A genetic module’s input/output behavior changes in unpredictable ways upon inclusion into a larger system. Therefore, each component of a system is usually redesigned every time a new piece is added. Rather than relying on such ad-hoc design procedures, control theoretic approaches may be used to engineer “insulation” of circuit components from context, thus enabling modular composition through specified input/output connections. In this talk, I will give an overview of modularity failures in genetic circuits, focusing on problems of loads, and introduce a controltheoretic framework, founded on the concept of retroactivity, to address the insulation question. Within this framework, insulation can be mathematically formulated as a disturbance rejection problem; however, classical solutions are not directly applicable due to bio-physical constraints. I will thus introduce solutions relying on time-scale separation, a key feature of biomolecular systems, which were used to build two devices: the load driver and the resource decoupler. These devices aid modularity, facilitate predictable composition of genetic circuits, and show that control theoretic approaches may be suitable to address pressing challenges in engineering biology.
Biography: Domitilla Del Vecchio received the Ph. D. degree in Control and Dynamical Systems from the California Institute of Technology, Pasadena, and the Laurea degree in Electrical Engineering (Automation) from the University of Rome at Tor Vergata in 2005 and 1999, respectively. From 2006 to 2010, she was an Assistant Professor in the Department of Electrical Engineering and Computer Science and in the Center for Computational Medicine and Bioinformatics at the University of Michigan, Ann Arbor. In 2010, she joined the Department of Mechanical Engineering at the Massachusetts Institute of Technology (MIT), where she is currently Professor and member of the Synthetic Biology Center. She is a IEEE Fellow and a recipient of the Newton Award for Transformative Ideas during the COVID-19 Pandemic (2020), the 2016 Bose Research Award (MIT), the Donald P. Eckman Award from the American Automatic Control Council (2010), the NSF Career Award (2007), the American Control Conference Best Student Paper Award (2004), and the Bank of Italy Fellowship (2000). Her research focuses on developing techniques to make synthetic genetic circuits robust to context and on applying these to biosensing and cell fate control for regenerative medicine applications.
Seminar 4: Urban Air Mobility: A Control-Centric Approach to Addressing Technical Challenge (Dr. Irene M. Gregory)
WebEx Link: https://force.my.webex.com/force.my/j.php?MTID=m57ea82432c42e3e86ab4df910933de7a
Meeting number (access code): 182 878 5141
Meeting password: M2QeJTSHa67 (62735874 from phones and video systems)
Abstract: Urban Air Mobility (UAM) is an emerging aviation sector and is playing an integral part in the on-demand mobility revolution. UAM is powered by the convergence of advances in distributed electrical propulsion (DEP) and vehicle autonomy. The complexity of operations in the urban environment and the unconventional vehicle configurations designed to take advantage of new propulsion technologies, result in numerous challenges that benefit from a control-centric approach. In this talk we outline some of these challenges and present our current approach to addressing them. For example, in order to achieve full market potential and access to UAM, vehicle autonomous flight is required. A key barrier to autonomous flight in a large multi-agent system is dealing with off-nominal situations and contingencies in a safe and predictable manner. We present our approach to intelligent contingency management, and share recent results and open problems. Additionally, we discuss another major barrier to ubiquitous UAM – the noise signature produced by vehicles with multiple rotors. We present our approach to minimizing such noise within the framework of the acoustically-aware vehicle.
Biography: Dr. Irene M. Gregory is the NASA Senior Technologist for Advanced Control Theory and Applications. Dr. Gregory received a S.B, in Aeronautics and Astronautics form MIT and a Ph.D. in Control and Dynamics Systems from Caltech. Her research has spanned the entire flight regime from hypersonic vehicles to slow subsonic UAVs with unconventional configurations. She is an author of over 100 referenceable publications. Her current interests are in the areas of robust autonomous systems, self-aware vehicle intelligent contingency management, acoustically-aware vehicles, and resilient control for advanced, unconventional configurations with particular focus on Urban Air Mobility and autonomous cargo. She is a Fellow of the AIAA, a senior member of IEEE and a member of IFAC; and, serves on IEEE Control Systems Society Aerospace Control and Intelligent Control Technical Committees as well as on AIAA Guidance, Navigation, and Control Technical Committee.
Seminar 5: Control and Optimization as a Foundation for Multi-UAV Coordination (Dr. David Casbeer)
WebEx Link: https://force.my.webex.com/force.my/j.php?MTID=mff686ca99351a8667e55da862aaba992
Meeting number (access code): 182 211 1784
Meeting password: eZuy9TYpx25 (39899897 from phones and video systems)
Abstract: In this talk we will discuss how optimization and control theory play a fundamental, and often overlooked, role in multi-UAV coordination. We will see how the solutions of optimal control problems are essential in combinatorial assignment algorithms. Using intuition gained by solving these problem, one can intuit how results dealing with static task assignment extend to cases where the tasks are dynamic in nature. The concepts discussed in this talk will be highlighted with specific problems that are relevant to defense applications.
Biography: David Casbeer is the Team Lead over Cooperative & Intelligent UAV Control with the Control Science Center of Excellence, Aerospace Systems Directorate, Air Force Research Laboratory. In this capacity, he conducts and leads basic research in cooperative control and decision making of autonomous UAVs, with a particular emphasis on high-level decision making and planning under uncertainty. He received B.S. and Ph.D. degrees in Electrical Engineering from Brigham Young University in 2003 and 2009, respectively. He is a former chair of the AIAA Intelligent Systems technical committee. He currently serves as a Senior Editor for the Journal of Intelligent and Robotic Systems and an Associate Editor for the AIAA Journal of Aerospace Information Systems.
