Go to JKU Homepage
Institute of Computer Graphics
What's that?

Institutes, schools, other departments, and programs create their own web content and menus.

To help you better navigate the site, see here where you are at the moment.


Amanda Prorok
University of Cambridge

Using Graph Neural Networks to Learn to Communicate, Cooperate, and Coordinate in Multi-Robot Systems

Date: January 30th, 2024, 2:00 pm CET

Room: HS 1 / Zoom

Abstract: How are we to orchestrate large teams of agents? How do we distill global goals into local robot policies? Machine learning has revolutionized the way in which we address these questions by enabling us to automatically synthesize decentralized agent policies from global objectives. In this presentation, I first describe how we leverage data-driven approaches to learn interaction strategies that lead to coordinated and cooperative behaviors. I will introduce our work on Graph Neural Networks, and show how we use such architectures to learn multi-agent policies through differentiable communications channels. I will present some of our results on cooperative perception, coordinated path planning, and close-proximity quadrotor flight. To conclude, I discuss the impact of policy heterogeneity on agent alignment and sim-to-real transfer.

About the Speaker: Amanda Prorok is Professor of Collective Intelligence and Robotics in the Department of Computer Science and Technology, at Cambridge University, and a Fellow of Pembroke College. Her lab's research focuses on multi-agent and multi-robot systems. Their mission is to find new ways of coordinating artificially intelligent agents (e.g., robots, vehicles, machines) to achieve common goals in shared physical and virtual spaces. Together with her lab, Prorok pioneered methods for differentiable communication between learning agents. Their research brings in methods from machine learning, planning, and control, and has numerous applications, including automated transport and logistics, environmental monitoring, surveillance, and search.

Prior to joining Cambridge, Amanda was a postdoctoral researcher at the General Robotics, Automation, Sensing and Perception (GRASP, opens an external URL in a new window) Laboratory at the University of Pennsylvania, USA. She completed her PhD at EPFL, opens an external URL in a new window, Switzerland. She has been honored by numerous research awards, including an ERC Starting Grant, an Amazon Research Award, the EPSRC New Investigator Award, the Isaac Newton Trust Early Career Award, and several Best Paper awards. Her PhD thesis was awarded the Asea Brown Boveri (ABB) prize for the best thesis at EPFL in Computer Science. She serves as Associate Editor for IEEE Robotics and Automation Letters (R-AL) and Associate Editor for Autonomous Robots (AURO). 

We would like to point out that when playing the video, data may be transmitted to external parties. Learn more by reading our data privacy policy

Michael Burch
University of Applied Sciences Graubünden

Eye Tracking in Visual Analytics

Date: January 10th, 2024, 2:00 pm CET

Room: S3 055 / Zoom

Abstract: Visual analytics tools are complex visual interfaces that can be inspected from many perspectives like the visualizations, user interface components, interaction techniques, displays, algorithmic techniques, but even more the users - expert or non-expert ones - with their experience levels and tasks-at-hand. No matter how complex such a visual analytics tool is and on what application field its focus lies, user evaluation is a powerful concept to investigate whether the tool is understandable and useful or creates challenges on the users' sides due to many design flaws. Eye tracking is getting more and more prominent in visual analytics to understand user behavior based on visual attention and visual scanning strategies. However, the recorded eye movement data creates a new complex data source for which visual analytics is required again to find patterns, anomalies, insights, and knowledge in the eye movement data.

About the Speaker: Michael Burch studied computer science and mathematics at the Saarland University in Saarbrücken, Germany. He received his PhD from the University of Trier in 2010 in the fields of information visualization and visual analytics. After 8 years of having been a PostDoc in the Visualization Research Center (VISUS) in Stuttgart, he moved to the Eindhoven University of Technology (TU/e) as an assistant professor for visual analytics. From October 2020 he has been working as a lecturer in visualization at the University of Applied Sciences in Chur, Switzerland. Michael Burch is on many international program committees and has published more than 190 conference papers and journal articles in the field of visualization. His main interests are in information visualization, visual analytics, eye tracking, and data science.

We would like to point out that when playing the video, data may be transmitted to external parties. Learn more by reading our data privacy policy