Prof. Wolfram Burgard
University of Freiburg, Germany Web: Wolfram Burgard
Probabilistic and Machine Learning Approaches for Autonomous Robots and Automated Driving
For autonomous robots and automated driving, the capability to robustly perceive their environments and execute their actions is the ultimate goal. The key challenge is that no sensors and actuators are perfect, which means that robots and cars need the ability to properly deal with the resulting uncertainty. In this presentation, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology to solve the state estimation problem. I will furthermore discuss how this approach can be extended using state-of-the-art technology from machine learning to bring us closer to the development of truly robust systems able to serve us in our every-day lives.
Wolfram Burgard is a full professor for Computer Science at the University of Freiburg where he heads the research lab for Autonomous Intelligent Systems. He studied Computer Science at the University of Dortmund and received his PhD in Computer Science from the University of Bonn. From 2019-2021 he was VP for Automated Driving Technology at the Toyota Research Institute. Wolfram Burgard is known for his contributions to the problem of mobile robot navigation, in particular with his probabilistic approaches to the problem of simultaneous localization and mapping. Wolfram Burgard is Fellow of the Association for the Advancement of Artificial Intelligence (AAAI), the European Association for Artificial Intelligence (EurAI) and the Institute of Electrical and Electronics Engineers (IEEE). He furthermore is member of the German Academy of Sciences Leopoldina and the Heidelberg Academy of Sciences. He received an Advanced Grant of the European Research Council (ERC) and the Gottfried Wilhelm Leibniz Prize, the most prestigious German research award.
Prof. Danica Kragic
KTH Royal Institute of Technology, Sweden Web: Danica Kragic
Learning perception, action and interaction
All day long, our fingers touch, grasp and move objects in various media such as air, water, oil. We do this almost effortlessly - it feels like we do not spend time planning and reflecting over what our hands and fingers do or how the continuous integration of various sensory modalities such as vision, touch, proprioception, hearing help us to outperform any other biological system in the variety of the interaction tasks that we can execute. Largely overlooked, and perhaps most fascinating is the ease with which we perform these interactions resulting in a belief that these are also easy to accomplish in artificial systems such as robots. However, there are still no robots that can easily hand-wash dishes, button a shirt or peel a potato. Our claim is that this is fundamentally a problem of appropriate representation or parameterization. When interacting with objects, the robot needs to consider geometric, topological, and physical properties of objects. This can be done either explicitly, by modeling and representing these properties, or implicitly, by learning them from data. The main objective of our work is to create new informative and compact representations of deformable objects that incorporate both analytical and learning-based approaches and encode geometric, topological, and physical information about the robot, the object, and the environment. We do this in the context of challenging multimodal, bimanual object interaction tasks. The focus will be on physical interaction with deformable and soft objects.
Danica Kragic is a Professor at the School of Computer Science and Communication at the Royal Institute of Technology, KTH. She received MSc in Mechanical Engineering from the Technical University of Rijeka, Croatia in 1995 and PhD in Computer Science from KTH in 2001. She has been a visiting researcher at Columbia University, Johns Hopkins University and INRIA Rennes. She is the Director of the Centre for Autonomous Systems. Danica received the 2007 IEEE Robotics and Automation Society Early Academic Career Award. She is a member of the Royal Swedish Academy of Sciences, Royal Swedish Academy of Engineering Sciences and Young Academy of Sweden. She holds a Honorary Doctorate from the Lappeenranta University of Technology. Her research is in the area of robotics, computer vision and machine learning. She received ERC Starting and Advanced Grant. Her research is supported by the EU, Knut and Alice Wallenberg Foundation, Swedish Foundation for Strategic Research and Swedish Research Council. She is an IEEE Fellow.
Prof. Takayuki Kanda
Kyoto University, Japan Web: Takayuki Kanda
Social robots in public space
Abstract: Social robots are coming to appear in our daily lives. Yet, it is not as easy as one might imagine. We developed a human-like social robot, Robovie, and studied the way to make it serve for people in public space, such as a shopping mall. On the technical side, we developed a human-tracking sensor network, which enables us to robustly identify locations of pedestrians. Given that the robot was able to understand pedestrian behaviors, we studied various human-robot interaction in the real-world. We faced with many of difficulties. For instance, the robot failed to initiate interaction with a person, and it failed to coordinate with environments, like causing a congestion around it. Toward these problems, we have modeled various human interaction. Such models enabled the robot to better serve for individuals, and also enabled it to understand people’s crowd behavior, like congestion around the robot; however, it invited another new problem, robot abuse. I plan to talk about a couple of studies in this line, and some of successful services provided by the social robot in the shopping mall, hoping to provide an insight about what the social robots in public space in a near future will be.
Takayuki Kanda is a professor in Informatics at Kyoto University, Japan. He is also a Visiting Group Leader at ATR Intelligent Robotics and Communication Laboratories, Kyoto, Japan. He received his B. Eng, M. Eng, and Ph. D. degrees in computer science from Kyoto University, Kyoto, Japan, in 1998, 2000, and 2003, respectively. He is one of the starting members of Communication Robots project at ATR. He has developed a communication robot, Robovie, and applied it in daily situations, such as peer-tutor at elementary school and a museum exhibit guide. His research interests include human-robot interaction, interactive humanoid robots, and field trials.
Prof. Dana Kulic
Monash University, Australia Web: Dana Kulic
Learning from Human-Robot Interaction
Robots working in human environments need to learn from and adapt to their users. In this talk, I will describe the challenges of robot learning during human-robot interaction: what should be learned? how can a user effectively provide feedback and input? I will illustrate the challenges with examples of robots in different roles and applications, including rehabilitation, collaboration in industrial and field settings, and in education and entertainment.
Prof. Dana Kulić conducts research in robotics and human-robot interaction (HRI), and develops autonomous systems that can operate in concert with humans, using natural and intuitive interaction strategies while learning from user feedback to improve and individualize operation over long-term use. Dana Kulić received the combined B. A. Sc. and M. Eng. degree in electro-mechanical engineering, and the Ph. D. degree in mechanical engineering from the University of British Columbia, Canada, in 1998 and 2005, respectively. From 2006 to 2009, Dr. Kulić was a JSPS Post-doctoral Fellow and a Project Assistant Professor at the Nakamura-Yamane Laboratory at the University of Tokyo, Japan. In 2009, Dr. Kulić established the Adaptive System Laboratory at the University of Waterloo, Canada, conducting research in human robot interaction, human motion analysis for rehabilitation and humanoid robotics. Since 2019, Dr. Kulić is a professor and director of Monash Robotics at Monash University, Australia. Dr. Kulić holds an Australian Research Council Future Fellowship. Her research interests include robot learning and human-robot interaction.