Plenary Speakers

Aníbal Ollero
Wednesday, November 23 16:40 Aula Magna

Aerial Manipulators and Bioinspired Robots

This presentation deals with two relevant topics in aerial robotics that are converging: Aerial Manipulation and Bioinspired Aerial Robots.

I will present recent results in aerial robotics manipulation, mainly obtained in the H2020 AERIAL-CORE project that I am coordinating. They will include not only aerial manipulation while flying, but also while holding with one arm and manipulating with other and also while perching.

Next I will present recent results in my ERC Advanced Grant GRIFFIN dealing with bioinspired (flapping-wing) aerial robots. I will present new more efficient prototypes and the first fully autonomous indoor and outdoor flapping wing robots with on-board perception for guidance and obstacle detection and avoidance. I will also present the fist fully autonomous perching of these flapping-wing robots.

Finally, I will point to the future, presenting preliminary results about manipulation with flapping-wing robots.


Anibal Ollero (Fellow, IEEE) is currently a Full Professor and the Head of the GRVC Robotics Laboratory, University of Seville, and the Scientific Advisor of the Center for Aerospace Technologies (CATEC), Seville. He has been a Full Professor at the Universities of Santiago and Malaga, Spain, and a Researcher at the Robotics Institute, Carnegie Mellon University, Pittsburgh, USA, and LAAS-CNRS, Toulouse, France. He has authored more than 750 publications, including nine books and 200 articles in journals and has been the editor of 15 books.

He has led more than 160 research projects, participating in more than 25 projects of the European Research Programmes being a Coordinator of seven and associated or the Deputy Coordinator of three, all of them dealing with unmanned aerial systems and aerial robots.

Since November 2018, he has been running the GRIFFIN ERC-Advanced Grant with the objective of developing a new generation of aerial robots that will be able to glide, flap the wings, perch, and manipulate by maintaining the equilibrium.

He has transferred technology to 20 companies and has been awarded with 23 international research and innovation awards, including the Overall Information and Communication Technologies Innovation Radar Prize 2017 of the European Commission and the recent Rey Jaume I in New Technologies (Spain). He has also been elected one of the three European innovators of the year, being candidate to the European personalities, in 2017 and IEEE Fellow "for contributions to the development and deployment of aerial robots."

He was a member of the “Board of Directors” of euRobotics, until March 2019. He was also the Founder and the President of the Spanish Society for the Research and Development in Robotics (SEIDROB), until November 2017. He is currently the Co-Chair of the IEEE Technical Committee on Aerial Robotics and Unmanned Aerial Vehicles, and the Coordinator of the Aerial Robotics Topic Group, euRobotics.

Estela Bicho
Thursday, November 24 9:20 Aula Magna

A Neuro-dynamics approach to Robots as Socially Intelligent Assistants/Co-workers: from the neurocognitive basis of joint action in humans to human-robot collaboration

As robot systems are moving as assistants into human everyday life, the question how to design robots capable of acting as sociable partners in collaborative joint activity becomes increasingly important. The capacity to anticipate and take into account action goals of a partner is considered a fundamental cognitive capacity for successful cooperative behaviour in a shared task. I will report about our approach towards creating socially intelligent robots that is heavily inspired by experimental and theoretical findings about the neurocognitive mechanisms underlying joint action in humans. We believe that designing cognitive control architectures on this basis will lead to more natural and efficient human-robot interaction/collaboration since the teammates will become more predictable for each other. Central to our approach, we use neuro-dynamics as a theoretical language to model cognition, learning, decision making and action. The robot control architecture is formalized by a coupled system of dynamic neural fields representing a distributed network of local but connected neural populations with specific functionalities. Different pools of neurons encode task relevant information about action means, action goals and context in form of self-sustained activation patterns. These patterns are triggered by input from connected populations and evolve continuously in time under the influence of recurrent interactions. The dynamic control architecture has been validated in tasks in which an anthropomorphic robot acts as a personal assistant in joint action tasks. We show that the context dependent mapping from action observation onto appropriate complementary actions allows the robot to cope with dynamically changing joint action situations. More specifically, the results illustrate crucial cognitive capacities for efficient and successful human-robot collaboration such as goal inference, emotion inference, error monitoring, anticipatory action selection, and learning.


Estela Bicho is a Full Professor at the Department of Industrial Electronics & Algoritmi Research Centre, School of Engineering (EEUM), at the University of Minho, Portugal, where she coordinates the Control, Automation and Robotics Group (Research line Industrial Electronics) and leads the MAR Lab - Mobile and Anthropomorphic Robotics Lab. She is currently vice-president of EEUM in charge of interaction with society.

She obtained her Ph.D. in Robotics, Automation, and Control in 1999 from the University of Minho, Portugal. In 1995-99 she was a member of the 'Equipe de Dynamique', at the 'Centre de Recherche en Neurosciences Cognitives' at CNRS in Marseille, France. She has been PI, or Co-PI, in several national and international ICT / Robotics projects. Her research focuses on uni- and multi-robot systems, human-robot interaction & collaboration, robot learning, robotic manipulation, autonomous navigation, intelligent vehicles aware of their occupants, medical robotics, and medical devices.

She received several awards and distinctions, e.g., her PhD work received the 1999 Portuguese IBM Honour Award; the video "The Power of Prediction: Robots that Read Intentions", Bicho et al, which summarised the work done by the UMinho team within the European project JAST was nominated for the six finalists of the Jubilee video award of the IROS (2012), an award that aimed to recognise works illustrating the history and/or milestones of intelligent robotics research in the last 25 years; in 2019, her PhD students, Weronika Wojtak, Flora Ferreira and Paulo Vicente, won a third prize in the elimination round of the "International Brain-Inspired Computing Competition" in Beijing, China, which brought together 200 teams from all over the world, where she and her team presented a robot that learns sequences about what to do and when, then uses them flexibly in various contexts of human-robot interaction and collaboration.

Recently, she was nominated by RoboHub as one of the "50 Women in Robotics you need to know about in 2021"

Wolfram Burgard
Thursday, November 24 15:00 Aula Magna

Probabilistic and Deep Learning Techniques for Robot Navigation and Automated Driving

For autonomous robots and automated driving, the ability to robustly perceive environments and execute their actions is the ultimate goal. The biggest challenge is that no sensors and actuators are perfect, which means that robots and cars must be able to properly deal with the resulting uncertainty. In this talk, I will introduce the probabilistic approach to robotics, which provides a rigorous statistical methodology for dealing with state estimation problems.

In addition, I will discuss how this approach can be extended using state-of-the-art machine learning technologies to deal with complex and changing real-world environments.


Wolfram Burgard is a professor for computer science at the University of Nuremberg and head of the research lab for Autonomous Intelligent Systems and former head of the research lab for Autonomous Mobile Systems at the University of Freiburg.

His research mainly focuses on the development of robust and adaptive techniques for state estimation and control. Over the past years he and his group have developed a series of innovative probabilistic techniques for robot navigation and control. They cover different aspects such as localization, map-building, SLAM, path-planning, exploration, and several other aspects.

Together with his colleagues, he developed numerous probabilistic approaches to mobile robot navigation. This includes Markov localization, a probabilistic approach to mobile localization. In 1999, he developed, together with Frank Dellaert, Dieter Fox and Sebastian Thrun the Monte Carlo localization, a probabilistic approach to mobile robot localization that is based on particle filters.

Wolfram Burgard and his group has also made substantial contributions to the simultaneous localization and mapping (SLAM) problem, which is to determine the map of the environment and the position of the robot at the same time.

In 2008, he became a fellow of the European Coordinating Committee for Artificial Intelligence. In 2009, Wolfram Burgard became fellow of the Association for the Advancement of Artificial Intelligence. In 2010, he received an Advanced Grant of the European Research Council.

He has obtained the 2009 Gottfried Wilhelm Leibniz Prize, the most prestigious German research prize. He has furthermore received seven best paper awards from outstanding conferences. He also became a distinguished lecturer of the IEEE Robotics and Automation Society.

He has published over 350 papers and articles in robotics and artificial intelligence conferences and journals and several books.

Margarita Chli

Friday, November 25 9:20 Aula Magna

Vision-based robotic perception: are we there yet?

As vision plays a key role in how we interpret a situation, developing vision-based perception for robots promises to be a big step towards robotic navigation and intelligence, with a tremendous impact on automating robot navigation.

This talk will discuss our recent progress in this area at the Vision for Robotics Lab of ETH Zurich (, and some of the biggest challenges we are faced with.


Margarita Chli received both her Bachelor and Master degrees in Information and Computing Engineering from Trinity College of the University of Cambridge, UK. In 2006, she moved to Imperial College London, UK, where she completed her PhD in applying Information Theory for efficient Simultaneous Localization And Mapping (SLAM), under the guidance of Prof. Andrew Davison.

In 2010, she joined the Autonomous Systems Lab of ETH Zurich as a Postdoctoral Researcher and later on, as a Lab Deputy Director. In 2013, she moved to the University of Edinburgh, UK to accept the prestigious Chancellor's Fellowship, before returning to ETH Zurich in 2015 to accept a Swiss National Science Foundation Assistant Professorship, while continuing to hold an Honorary Fellowship from the University of Edinburgh.

She is presently a professor for Computer Vision for Robotics at the Cyprus University of Technology and the director of the Vision for Robotics Lab at ETH Zurich.

Her research interests are in Computer Vision for Robotics, focusing on real-time perception for small aircraft, as some of the most challenging platforms for robotic perception.

Some highlights of her career include the participation in the first vision-based autonomous flight of a small helicopter, a mention in Robohub's 2016 list of 25 women in Robotics you need to know about and the award of the biannual Zonta Prize in 2017 on the basis of her high impact contributions on the development of robotic vision.

Moreover, her work at V4RL was featured in Reuters, while she was a speaker at the World Economic Forum in Davos in 2017 as part of ETH Zurich's 3-strong delegation of professors.