Calendar

Apr
7
Wed
LCSR Seminar: Robin Murphy “From the World Trade Center to the COVID-19 Pandemic: Robots and Disasters” @ https://wse.zoom.us/s/94623801186
Apr 7 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

Abstract:

This talk will describe how ground, aerial, and marine robots have been used in disasters, most recently the coronavirus pandemic. During the pandemic so far, 338 instances of robots in 48 countries protecting healthcare workers from unnecessary exposure, handling the surge in demand for clinical care, preventing infections, restoring economic activity, and maintaining individual quality of life have been reported.  The uses span six sociotechnical work domains and 29 different use cases representing different missions, robot work envelopes, and human-robot interaction dyads.  The dataset also confirms a model of adoption of robotics technology for disasters. Adoption favors robots that maximize the suitability for established use cases while minimizing risk of malfunction, hidden workload costs, or unintended consequences as measured by the NASA Technical Readiness Assessment metrics. Regulations do not present a major barrier but availability, either in terms of inventory or prohibitively high costs, does.  The model suggests that in order to be prepared for future events, roboticists should partner with responders now, investigate how to rapidly manufacture complex, reliable robots on demand, and conduct fundamental research on predicting and mitigating risk in extreme or novel environments.\

 

Biography:

Dr. Robin R. Murphy is the Raytheon Professor of Computer Science and Engineering at Texas A&M University, a TED speaker, and an IEEE and ACM Fellow. She helped create the fields of disaster robotics and human-robot interaction, deploying robots to 29 disasters in five countries including the 9/11 World Trade Center, Fukushima, the Syrian boat refugee crisis, Hurricane Harvey, and the Kilauea volcanic eruption. Murphy’s contributions to robotics have been recognized with the ACM Eugene L. Lawler Award for Humanitarian Contributions, a US Air Force Exemplary Civilian Service Award medal, the AUVSI Foundation’s Al Aube Award, and the Motohiro Kisoi Award for Rescue Engineering Education (Japan). She has written the best-selling textbook Introduction to AI Robotics (2nd edition 2019) and the award-winning Disaster Robotics (2014), plus serving an editor for the science fiction/science fact focus series for the journal Science Robotics. She co-chaired the White House OSTP and NSF workshops on robotics for infectious diseases and recently co-chaired the National Academy of Engineering/Computing Community Consortium workshop on robots for COVID-19.

 

Apr
21
Wed
LCSR Seminar: Gordon Berman “Measuring behavior across scales” @ https://wse.zoom.us/s/94623801186
Apr 21 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

Abstract:

When we think of animal behavior, what typically comes to mind are actions – running, eating, swimming, grooming, flying, singing, resting. Behavior, however, is more than the catalogue of motions that an organism can perform. Animals organize their repertoire of actions into sequences and patterns whose underlying dynamics last much longer than any particular behavior. How an organism modulates these dynamics affects its success at accessing food, reproducing, and myriad other tasks essential for survival. Animals regulate these patterns of behavior via many interacting internal states (hunger, reproductive cycle, age, etc.) that we cannot directly measure. Studying these hidden states’ dynamics, accordingly, has proven challenging due to a lack of measurement techniques and theoretical understanding. In this talk, I will outline our efforts to uncover the latent dynamics that underlie long timescale structure in animal behavior. Looking across a variety of organisms, we use a novel methodology to measure animals’ full behavioral repertoires to find the existence of a non-trivial form of long timescale dynamics that cannot be explained using standard mathematical frameworks. I will present how temporal coarse-graining can be used to understand how these dynamics are generated and how the found course-grained states can be related to the internal states governing behavior through a combination of machine learning techniques and dynamical systems modeling.  Inferring these hidden dynamics presents a new opportunity to generate insights into the neural and physiological mechanisms that animals use to select actions.

Biography:

Gordon J. Berman, Ph.D., Assistant Professor of Biology, Emory University Co-Director, Simons-Emory International Consortium on Motor Control Chair of Recruitment for the Emory Neuroscience Graduate Program . Our lab uses theoretical, computational, and data-driven approaches to gain quantitative insight into entire repertoires of animal behaviors, aiming to make connections to the neurobiology, genetics, and evolutionary histories and that underlie them. Get more information here.

 

Apr
28
Wed
LCSR Seminar: Ram Vasudevan “How I Learned to Stop Worrying and Start Loving Lifting to Infinite Dimensions” @ https://wse.zoom.us/s/94623801186
Apr 28 @ 12:00 pm – 1:00 pm

Link for Live Seminar

Link for Recorded seminars – 2020/2021 school year

 

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 including ground vehicles, manipulators, and walking robots. The second technique is a modeling method that allows one to represent a nonlinear system as a linear system in the infinite-dimensional space of real-valued functions. By applying this modeling method, one can employ well-understood linear model predictive control techniques to robustly control nonlinear systems. The utility of this approach is verified on a soft robot control task.

 

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 and has been finalist for best paper at Robotics: Science and Systems.

 

Johns Hopkins University

Johns Hopkins University, Whiting School of Engineering

3400 North Charles Street, Baltimore, MD 21218-2608

Laboratory for Computational Sensing + Robotics