The Robotics Colloquium features talks by invited and local researchers on all aspects of robotics, including control, perception, machine learning, mechanical design, and interaction. The colloquium is held Fridays between 1:30-2:30pm. Special seminars outside this schedule are indicated below. Refreshments are served.
If you would like to give a talk in upcoming Robotics Colloquia, please contact Maya Cakmak. If you would like to get regular email announcements and reminders about the robotics colloquium speakers, please sign up for the Robotics@UW mailing list.
Spring 2019 Organizers: Tapomayukh Bhattacharjee, Maya Cakmak, Dieter Fox, Siddhartha S. Srinivasa
Abstract: Robust information exchange and trusted coordination are both critical needs for multi-robot systems acting in the real world. While these needs are universal across platforms, the computing and sensing resources of these platforms are not – making effective coordination difficult to enable, to scale, and to secure. This talk will present new methods of security and adaptive network formation for resource-constrained, mobile multi-robot systems (applications include delivery drones, mobile IoT, and robotic vehicles). The focus of this work is at the intersection of robotics and communication, and in particular, we study ways that communication technologies can be used to make resource-constrained multi-robot systems more capable. This talk will touch upon our developed technologies in 1) position control algorithms for multiple robots to achieve high data rate networks and 2) development of a virtual sensor for bi-directional Synthetic Aperture Radar between two communicating agents. Building upon these technologies, we develop a theoretical and experimental framework for provably thwarting spoofing attacks using communicated wireless signals in various important multi-agent tasks such as consensus, coverage, and drone delivery. This talk will have a particular focus on our most recent results in securing multi-agent consensus.
Biography: Stephanie is currently an Assistant Professor in the School of Computing, Informatics, and Decision Systems Engineering at Arizona State University (Jan 2018). Prior, she was a research scientist in the Computer Science and Artificial Intelligence Lab (CSAIL) at MIT where she also completed her Ph.D. work (2014) on multi-robot coordination and control and M.S. work (2009) on system identification and model learning. At MIT she collaborated extensively with the wireless communications group NetMIT, the result of which were two U.S. patents recently awarded in adaptive heterogeneous networks for multi-robot systems and accurate indoor positioning using Wi-Fi. She completed her B.S. at Cornell University in 2006.
Abstract: Adaptability is an essential skill in human cognition, enabling us to draw from our extensive, life-long experiences with various objects and tasks in order to address novel problems. To date, most robots do not have this kind of adaptability, and yet, as our expectations of robots' interactive and assistive capacity grows, it will be increasingly important for them to adapt to unpredictable environments in a similar manner as humans. In this talk I will describe my approaches to the problem of task transfer, enabling a robot to transfer a known task model to address scenarios containing differences in the objects used, object configurations, and task constraints. The primary contribution of my work is a series of algorithms for deriving and modeling domain-specific task information from structured interaction with a human teacher. In doing so, this work enables the robot to leverage the teacher's domain knowledge of the task (such as the contextual use of an object or tool) in order to address a range of tasks without requiring extensive exploration or re-training of the task. By enabling a robot to ask for help in addressing unfamiliar problems, my work contributes toward a future of adaptive, collaborative robots.
Biography: Tesca Fitzgerald is a Computer Science PhD candidate in the School of Interactive Computing at the Georgia Institute of Technology. In her PhD, she has been developing algorithms and knowledge representations for robots to learn, adapt, and reuse task knowledge through interaction with a human teacher. In doing so, she applies concepts of social learning and cognition to develop a robot which adapts to human environments. Tesca is co-advised by Dr. Ashok Goel (director of the Design and Intelligence Lab) and Dr. Andrea Thomaz (director of the Socially Intelligent Machines Lab). Before joining Georgia Tech in 2013, she graduated from Portland State University with a B.Sc. in Computer Science. Tesca is an NSF Graduate Research Fellow (2014), Microsoft Graduate Women Scholar (2014), and IBM Ph.D. Fellow (2017).