Past Events

AEM Seminar: How to Make Your Ocean Smarter

Our oceans drive worldwide weather-climate systems; our rivers serve as nutrient conduits; and our marine ecosystems house the largest repository of biodiversity and mineral resources on the planet. Humans have relied on rivers, lakes, and oceans for transportation, energy generation, farming, and recreation throughout our history. And today, robots are critical tools in our stewardship of these resources. However, there are significant autonomy challenges when working in dynamic and uncertain environments like oceans and rivers. Robot dynamics are tightly coupled to those of the environment, while communication and localization are limited.

Control under these conditions can be exacting, but environmental dynamics may be harnessed to plan energy efficient paths and to maintain network connectivity. Networked robot teams can collect data to construct high fidelity models of the environmental dynamics which can be integrated into robot control and planning. Those same models can be used to guide robot control and sampling strategies to increase their predictive power. In this talk, I will present our vision of a smart ocean observational framework to improve forecasting of weather-climate systems, mitigation of contaminant dispersions, and coordination of maritime search and rescue and humanitarian efforts.
 
About Dr. M. Ani Hsieh

M. Ani Hsieh is a Research Associate Professor in the Department of Mechanical Engineering and Applied Mechanics at the University of Pennsylvania. She is also the Deputy Director of the General Robotics, Automation, Sensing, and Perception (GRASP) Laboratory. Her research interests lie at the intersection of robotics, multi-agent systems, and dynamical systems theory. Hsieh and her team design algorithms for estimation, control, and planning for multi-agent robotic systems with applications in environmental monitoring, estimation and prediction of complex dynamics, and design of collective behaviors. She received her B.S. in Engineering and B.A. in Economics from Swarthmore College and her PhD in Mechanical Engineering from the University of Pennsylvania. Prior to Penn, she was an Associate Professor in the Department of Mechanical Engineering and Mechanics at Drexel University. Hsieh is the recipient of a 2012 Office of Naval Research (ONR) Young Investigator Award and a 2013 National Science Foundation (NSF) CAREER Award.

Robotics 8970 Colloquium: Julianna Abel

Design and Manufacture of Multifunctional Yarns and Textiles

Highlighting recent advancements in the design and manufacture of yarns and textiles fabricated from shape memory alloys.

About Dr. Abel
Dr. Julianna Abel is a Benjamin Mayhugh Assistant Professor in the Department of Mechanical Engineering at the University of Minnesota. Dr. Abel earned her Ph.D. and M.S. in Mechanical Engineering from the University of Michigan and her B.S. from the University of Cincinnati. She is a NSF CAREER Award recipient, Toyota Programmable Systems Innovation Fellow, Glenn Research Center Faculty Fellow, and recently earned the 2020 ASME Ephrahim Garcia Best Paper Award. Her research combines innovative design processes and advanced manufacturing techniques with material and structural modeling to lay the scientific foundation necessary for the design of multifunctional yarns and textiles.

Robotics 8970 Colloquium: Suhasa Kodandaramaiah

See here at a later date for more details.

Robotics 8970 Colloquium: Changhyun Choi

See here at a later date for more details.

Robotics 8970 Colloquium: Brad Holshuh

Human Factors and Ergonomics at the University of Minnesota

View the preview video here.

AEM Seminar: Enabling Robots to Cooperate with Distributed Optimization and Compete with Game Theory

For robots to effectively operate in our world, they must master the skills of dynamic interaction. Autonomous cars must safely negotiate their trajectories with other vehicles and pedestrians as they drive to their destinations. UAVs must avoid collisions with other aircraft, as well as dynamic obstacles on the ground. Disaster response robots must coordinate to explore and map new disaster sites.

In this talk, Dr. Schwager will describe recent work in my lab using distributed optimization to obtain algorithms for robots to cooperate, and game-theoretic methods to obtain algorithms for robots to compete. He will describe a general and flexible method, called SOVA, for deriving distributed optimization algorithms for a variety of multi-robot collaborative problems. He will present an algorithm for fleets of autonomous cars to cooperatively track a large number of vehicles and pedestrians in a city, an algorithm for multiple robots to manipulate an object to a goal while avoiding collisions, and a distributed multi-robot SLAM algorithm, all derived using the SOVA method.

Additionally, Dr. Schwager will also discuss algorithms based on the theory of dynamic games, in which each actor has its own objective and constraints. I will describe examples in autonomous drone racing, car racing, and autonomous driving that use game theoretic principles to solve for Nash equilibrium trajectories in real-time, in a receding horizon fashion. Throughout the talk, he will show results from hardware experiments with ground robots, autonomous cars, and quadrotor UAVs collaborating and competing in the scenarios above.

About Dr. Schwager

Mac Schwager is an Assistant Professor of Aeronautics and Astronautics at Stanford University. He directs the Multi-robot Systems Lab (MSL) where he studies distributed algorithms for control, perception, and learning in groups of robots and autonomous systems. He is interested in a range of applications including cooperative surveillance with teams of UAVs, autonomous driving in traffic, cooperative robotic manipulation, distributed SLAM, distributed trajectory planning, and autonomous drone and car racing. He obtained his BS degree from Stanford, and his MS and PhD degrees from MIT. He was a postdoctoral researcher at the University of Pennsylvania and at MIT. Prior to joining Stanford, he was an assistant professor at Boston University from 2012 to 2015. He received the NSF CAREER award in 2014, the DARPA YFA in 2018, and has received numerous best paper awards in conferences and journals including the IEEE Transactions on Robotics best paper award in 2016, the Best Paper Award in Robot Manipulation in ICRA 2018, and the Best Paper Award in Multi-Robot Systems in ICRA 2020.

Robotics 8970 Colloquium: Nicholas Heller

"Characterizing Renal Tumors with 3D Semantic Segmentation"

Robotics 8970 Colloquium: Junaed Sattar

Perception, Learning, and Systems for Underwater Human-Robot Collaboration

This talk will present a brief overview of the IRV Lab's research and present an in-depth discussion of some recent projects in underwater human-robot interaction and imagery enhancement.

About Dr. Junaed Sattar

Sattar is an assistant professor at the Department of Computer Science and Engineering at the University of Minnesota and MnDrive (Minnesota Discovery, Research, and Innovation Economy) faculty. He is the founding director of the Interactive Robotics and Vision Lab, problems in field robotics, robot vision, human-robot communication, assisted driving, applied (deep) machine learning, and develop rugged robotic systems are investigated.

He received his graduate degrees from McGill University (Canada) and has a B.S. in Engineering from the Bangladesh University of Engineering and Technology. Before coming to the University of Minnesota, he worked as a post-doctoral fellow at the University of British Columbia, working on service and assistive robotics, as well as at Clarkson University (upstate New York) as an Assistant Professor.

You can find out more about this work on Dr. Sattar's personal website and the IRV Lab website. You can also follow @IRVLab on Twitter or visit the IRV Lab Youtube page.

 

Robotics 8970 Colloquium: MnDOT Robotics

MnDOT scientists and MnRI collaborators Ted Morris and Kule Hoegh give a rundown of their research for MnDOT.