Keynote Speakers


Keynote Speakers


Prof. Dr. Wolfram Burgard

Wolfram Burgard is a professor for computer science at the University of Freiburg, Germany where he heads the Laboratory for Autonomous Intelligent Systems. He studied Computer Science at the University of Dortmund and received his Ph.D. degree in computer science from the University of Bonn in 1991. His areas of interest lie in artificial intelligence and mobile robots. In the past, Wolfram Burgard and his group developed several innovative probabilistic techniques for robot navigation and control. They cover different aspects including localization, map-building, path planning, and exploration. He received the prestigious Gottfried Wilhelm Leibniz-Preis in 2009 and an advanced ERC grant in 2010. He is an AAAI and ECCAI fellow.

Probabilistic and Deep Learning Techniques for Mobile Robot Navigation and Autonomous Driving

October 11th, 2021 at 11AM, (Natal local time)
For autonomous robots and automated driving, the capability to robustly perceive 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 deal with state estimation problems. I will furthermore discuss how this approach can be extended using state-of-the-art technology from machine learning to deal with complex and changing real-world environments.

Prof. Dr.-Ing. Rüdiger Dillmann

Rüdiger Dillmann received his Ph. D. from University of Karlsruhe in 1980. Since 1987 he has been Professor of the Department of Computer Science and is Director of the Research Lab. Humanoids and Intelligence Systems at KIT. 2002 he became director of an innovation lab. at the Research Center for Information Science (FZI), Karlsruhe. 2009 he founded the Institute of Anthropomatics and Robotics at the Karlsruhe Institute of Technology. His research interest is in the areas of human-robot interaction, neurorobotics with special emphasis on intelligent, autonomous and interactive robot behaviour generated with the help of machine learning methods and programming by demonstration (PbD). Other research interests include machine vision for mobile systems, man-machine cooperation, computer supported intervention in surgery and related simulation techniques. He is author/co-author of more than 1000 scientific publications, conference papers, several books and book contributions. He was Coordinator of the German Collaborative Research Center ”Humanoid Robots”, SFB 588 and several European IPs. He is Editor of the journal ”Robotics and Autonomous Systems”, Elsevier, and Editor in Chief of the book series COSMOS, Springer. He is IEEE Fellow and IROS Fellow.

Building Brains for Robots: Neuromorphic SNN-based Controls for Robot Visuomotor Tasks

October 12th, 2021 at 11AM, (Natal local time)
The long term goal of this research within the framework of the European Human Brain Project (HBP) is to understand, to model and to translate biomorphic neural principles towards biocybernetic robot control systems. In comparison to conventional computing the brain is superior in terms of energy efficiency, time, robustness and adaptivity. Thus, we investigate into modeling biologic processes enabling the brain to perform sensomotoric computation and finally to implement it in silicon in form of biomorphic hardware. Todays neuromorphic hardware consists of spiking neural networks (SNNs) which can perform fast and efficient computations with continuous input - output streams based on synaptic plasticity. We focus on brain like senso-motor control tasks and ground them with the help of real robots. Spiking neural networks have the potential on replicating real neurons reflecting parts of their biological characteristics. SNNs are capable to perform synaptic spike-based communication with local brain functionalities supporting learning with the help of neural plasticity mechanisms. We assume, that the brain is forming sensor-motor primitives within building blocks composed for object detection, localization, event prediction, and finally the generation and execution of motion and interaction. The combination of neural motion primitives represent complex muscle motor synergies with the potential to learn complex large scale motions. Our SNN control architecture is capable to perform tasks like object recognition, object tracking, target reaching and grasping as well as collision- and obstacle avoidance. Closing the visuomotor loop by mapping the learned visual representation to motor commands show that SNNs learn without any planning algorithms nor inverse kinematics. SNNs are event driven and model free. We introduce deep continuous local learning mechanisms achieving state of the art robot accuracy on event stream benchmarks. Biologically plausible reward-learning rules based on synaptic sampling show that SNNs are capable of learning policies and various movement characteristics. Links between reward-modulated synaptic plasticity and online reinforcement learning show proposing results. The hyper-parameters of this neuromodulation and their impact on performance are to be discussed with the help of some closed-loop sensorimotor experiments. The potential of deep reinforcement learning for target reaching affects object interaction, manipulation and grasping tasks and allows its realtime execution within timevariant situations. An event-driven binocular DVS system is used in stereo mode driven by micro saccades. The spiking feedback information from the DVS and from proprioception is mapped towards motion generating SNNs applying reward coupling and prediction error minimization techniques. Future work towards the effective use of neuromorphic vision with emphasis to eye movement, micro saccades, visual affordance learning and high performance event prediction will be discussed. In addition it can be shown, that the brain-inspired computational paradigm can be extended towards SNN based navigation and mapping (BSLAM) forming episodic spatial neural memories with multi-scale learning capabilities. A software framework for developing and programming the related SNN-clusters and a neural robot platform (NRP) is to be presented.

Prof. Dr. Carme Torras Genís

Carme Torras is Research Professor at the Spanish Scientific Research Council (CSIC). She received M.Sc. degrees in Mathematics and Computer Science from the Universitat de Barcelona and the University of Massachusetts, respectively, and a Ph.D. degree in Computer Science from the Technical University of Catalonia (UPC). She is IEEE Fellow, EurAI Fellow, member of Academia Europaea, member of the Reial Acadèmia de Ciències i Arts de Barcelona, and she was Editor of the IEEE Trans. on Robotics.

Assistive Robotics: AI Challenges, Ethics Education and Science Fiction

October 13th, 2021 at 11AM, (Natal local time)
Assistive robotics is a fast growing field aimed at helping caregivers in hospitals, rehabilitation centers and nursing homes, as well as empowering people with reduced mobility at home, so that they can live autonomously. Most tasks assistive robots need to perform (e.g., helping users to dress, guiding rehabilitation, feeding) require dexterous manipulation skills, which need to be easily taught and customized by non-experts. In addition, such skills must be very compliant and intrinsically safe to people, as well as able to deal with deformable materials like clothing. Some results of projects addressing these demanding challenges, such as CLOTHILDE and SOCRATES, will be showcased. In addition to technoscientific challenges, assistive robotics poses ethical defies, which have led to the emergence of the discipline Roboethics. Several institutions are developing regulations, and a multitude of ethical education initiatives have emerged. Especially in technological degrees, science fiction readings and scenarios are used to motivate students and encourage debate. My book The Vestigial Heart (MIT Press, 2018) is being used in some of these university courses to illustrate the benefits and risks of human-robot interaction and how to preserve human values in situations of assistance.

Prof. Dr. Flavio Tonidandel

Associate Professor at the Department of Computer Science, Centro Universitario FEI (FEI-SP), Sao Paulo, Brazil. Received the B. Sc. degree in 1996 in Electronic Engineering from Universidade Federal de Uberlândia, Brazil, the M.Sc. degree in Electrical Engineering from POLI-USP in 1997, and the Ph.D. degree in Electrical Engineering at the same university, in January 2003, in the Artificial Intelligence area. His main research topics of interest are in the Intelligent Robotics field. He coordinates the Robotics Group ROBOFEI at FEI-SP, Brazil and is currently a Trustee of Robocup.

The progress of robotics in the world influenced by RoboCup in the last and the next few years

October 14th, 2021 at 11AM, (Natal local time)
RoboCup is the world's largest intelligent mobile robotics event. New technologies, concepts, and solutions are researched and developed in its dozens of categories. Let's talk about how robotics evolved in the world by RoboCup, how it influenced the emergence of robotic solutions around the planet, how they drive research and developments in universities, and how it will still affect the robot revolution in the coming years.