Talk 1: Perception and Control for Autonomous Systems
Talk 2: Efficient Perception and Learning with Symmetry and Active Sensing
Prof. Kostas Daniilidis, Ruth Yalom Stone Professor of Computer and Information Science, UPenn
2pm to 3pm: Perception and Control for Autonomous Systems
Abstract:
This talk will survey some of the work done in our group towards the goal of developing autonomous mobile robots that are more robust and capable. Specifically it will describe work that we have done on geometric and semantic scene understanding for ground robots and will discuss the representations and algorithms that we have developed to allow teams of air and ground vehicles to autonomously map and navigate environments spanning several square kilometers. The talk will also describe some recent work on a surprisingly effective approach to visual concept recognition which was developed to allow autonomous systems to reason about the properties of previously unseen objects that it encounters. Lastly the talk will discuss new approaches we have developed for modeling closed loop dynamical systems using neural networks. We show how these neural models can be used to develop adaptive control systems that can rapidly optimize the performance of our robots online to achieve greater levels of robustness to changing conditions.
Bio:
Dr. Taylor received his A.B. degree in Electrical Computer and Systems Engineering from Harvard College in 1988 and his M.S. and Ph.D. degrees from Yale University in 1990 and 1994 respectively. Dr. Taylor was the Jamaica Scholar in 1984, a member of the Harvard chapter of Phi Beta Kappa and held a Harvard College Scholarship from 1986-1988. From 1994 to 1997 Dr. Taylor was a postdoctoral researcher and lecturer with the Department of Electrical Engineering and Computer Science at the University of California, Berkeley. He joined the faculty of the Computer and Information Science Department at the University of Pennsylvania in September 1997. He received an NSF CAREER award in 1998 and the Lindback Minority Junior Faculty Award in 2001. In 2012 he received a best paper award at the IEEE Workshop on the Applications of Computer Vision. Dr Taylor's research interests lie primarily in the fields of Computer Vision and Robotics and include: reconstruction of 3D models from images, vision-guided robot navigation and scene understanding. Dr. Taylor has served as an Associate Editor of the IEEE Transactions of Pattern Analysis and Machine Intelligence. He has also served on numerous conference organizing committees. He was a General Chair of the International Conference on Computer Vision (ICCV) 2021 and served as a Program Chair of the 2006 and 2017 editions of the IEEE Conference on Computer Vision and Pattern Recognition and the 2013 edition of 3DV. In 2012 he was awarded the Christian R. and Mary F. Lindback Foundation Award for Distinguished Teaching at the University of Pennsylvania.
3pm to 4pm: Efficient Perception and Learning with Symmetry and Active Sensing
Abstract:
Scaling up data and computation is regarded today as the key to achieving unprecedented performance in many perception tasks. Biological perception is characterized, though, by principles of efficiency implemented through symmetry and efficient sensing. By respecting the symmetries of the problem at hand, models can generalize better, often requiring fewer parameters and less data to learn effectively. Moreover, they provide insights into the underlying structures and symmetries of the data, which can be invaluable in developing more robust and interpretable models. The incoming sensing bandwidth is remarkably low for vision in biological brains, while current computer vision systems are based on full video frames and many views of the world. We will present an active approach to view and touch selection based on information-theoretic principles. We will finish with a new sensor paradigm that senses only visual events rather than whole scenes and show how it can solve basic tasks fundamental to embodied intelligence.
Bio: Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania, where he has been faculty since 1998. He is an IEEE Fellow. He was the director of the GRASP laboratory from 2008 to 2013, Associate Dean for Graduate Education from 2012 to 2016, and Faculty Director of Online Learning from 2013 to 2017. He obtained his undergraduate degree in Electrical Engineering from the National Technical University of Athens in 1986 and his PhD in Computer Science from the University of Karlsruhe in 1992 under the supervision of Hans-Hellmut Nagel. He received the Best Conference Paper Award at the 2017 IEEE International Conference on Robotics and Automation (ICRA 2017). He is the recipient of the 2025 Provost’s Award for Distinguished PhD Mentoring and Teaching. He was Program co-Chair at ECCV 2010 and 3DPVT (now 3DV) 2006. His most cited works are on event-based vision, equivariant learning, 3D human pose, and hand-eye calibration.