15th International Workshop on Human-Friendly Robotics
Delft, The Netherlands, 22-23 September 2022
KEYNOTE SPEAKERS
ARE ROBOTS OUR FRIENDS?
Abstract: Computing power doubles every two years, and is called Moore’s Law. This exponential rate of change enables accelerating developments in sensor technology, AI computing and in robotics and automotive. Machines to make products in modern factories will be smart and self-learning. Cars will become like an iPad on wheels. We are world champion in soccer playing robots. The question is when will they be better than humans? What we learn with playing robot soccer is also applicable to service robotics for care at home, and to autonomous guided vehicles for agriculture, logistics and industrial applications. We learn our robots to navigate, but when will robots start to learn to us. Are humans in the end necessary? And how does the future of schools and universities look like?
Short bio: Maarten Steinbuch (born 1960 in Zeist, NL) is a high-tech systems scientist, entrepreneur and communicator. He holds the chair of Systems & Control at Eindhoven University of Technology (TU/e), where he is Distinguished University Professor. He is also Scientific Director of Eindhoven Engine. The research of his group spans from automotive engineering (with a focus on connected cars and clean vehicles) to mechatronics, motion control, and fusion plasma control. He is most known for his work in the field of advanced motion control and mechatronics, as well as in surgical robotics. Steinbuch is a prolific blogger and a key opinion leader on the influence of new technologies on society.
ROBOTS WHICH LEARN COMPLEX TASKS
Abstract: Abstract: Robotics research community has shown increased interest on robot skill learning in the past decade. Robot learning from imitating successful human demonstrations provides an efficient way to learn new skills, which can reduce time and cost to program the robot. However, the techniques for robot learning from demonstrations are often limited to learning simple movement primitives. In this talk, I will review some of the background, motivations and state of the art in robot learning from demonstrations towards complex task learning. I consider complex tasks as one with some level of complexity in task structure, which requires cognitive reasoning. In this regard, I will introduce some of recent progress which we made in our lab for bridging the low level skill learning and task knowledge. In this talk I will discuss spatial, temporal, and conditional structures of several robotic complex tasks.
Short bio: Dongheui Lee is Full Professor of Autonomous Systems at Institute of Computer Tech, Faculty of Electrical Engineering and Information Technology, TU Wien. She is also leading a Human-centered assistive robotics group at the German Aerospace Center (DLR). Her research interests include human motion understanding, human robot interaction, machine learning in robotics, and assistive robotics. Prior to her appointment at TU Wien, she was Associate Professor of Human-centered Assistive Robotics at the TUM Department of Electrical and Computer Engineering (2017-2022), Assistant Professor of Dynamic Human Robot Interaction at TUM (2009-2017), Project Assistant Professor at the University of Tokyo (2007-2009), and a research scientist at the Korea Institute of Science and Technology (KIST) (2001-2004). She obtained a PhD degree (2007) from the department of Mechano-Informatics, University of Tokyo in Japan and B.S. (2001) and M.S. (2003) degrees in mechanical engineering at Kyung Hee University, Korea. She was awarded a Carl von Linde Fellowship at the TUM Institute for Advanced Study (2011) and a Helmholtz professorship prize (2015). She has served as Senior Editor and a founding member of IEEE Robotics and Automation Letters (RA-L) and Associate Editor for the IEEE Transactions on Robotics.
ROBOTS LEARNING THROUGH INTERACTIONS
Abstract: The acquisition and self-improvement of novel motor skills is among the most important problems in robotics. Complexity arises from interactions with their environment and humans, dealing with high-dimensional input data, non-linear dynamics in general and contacts in particular, multiple reference frames, and variability in objects, environments, tasks, and human behavior. A human teacher is always involved in the learning process, either directly (providing data) or indirectly (designing the optimization criterion), which raises the question: How to best make use of the interactions with the human teacher to render the learning process efficient and effective? In this talk I’ll argue that there are tremendous benefits in having a human teacher intermittently interact with a robot also while it is learning. I will discuss various methods we have developed in the fields of supervised learning, imitation learning, reinforcement learning, and interactive learning. All these concepts will be illustrated with benchmark tasks and real robot experiments ranging from fun (ball-in-a-cup) to more applied (retail environments).
Short bio: Jens Kober is an associate professor at the TU Delft, Netherlands. He worked as a postdoctoral scholar jointly at the CoR-Lab, Bielefeld University, Germany and at the Honda Research Institute Europe, Germany. He graduated in 2012 with a PhD Degree in Engineering from TU Darmstadt and the MPI for Intelligent Systems. For his research he received the annually awarded Georges Giralt PhD Award for the best PhD thesis in robotics in Europe, the 2018 IEEE RAS Early Academic Career Award, the 2022 RSS Early Career Award, and has received an ERC Starting grant. His research interests include motor skill learning, (deep) reinforcement learning, imitation learning, interactive learning, and machine learning for control.