Program and Speakers
Individual Robot Perception and Learning
Cornelia Fermueller works in the areas of Computer, Human and Robot Vision at the University of Maryland at College Park. She studies and develops biologically inspired Computer Vision solutions for systems interacting with their environment. In recent years, her work has focused on the interpretation of human activities, and on motion processing for fast active robots using as input bio-inspired event-based sensors.
Georg Martius works as group leader at the Max Planck Institute for intelligent Systems. He is interested in autonomous learning, i.e. how an embodied agent can determine what to learn, how to learn, and how to judge its learning success. He believes that robots need to learn from experience to become dexterous and versatile assistants to humans in many real-world domains. Intrinsically motivated learning can help to create a suitable learning curriculum and lead to capable systems without the need to specify every little detail of that process. Here we take inspiration from child development.
Guido de Croon works at Delft University. Small, light–weight flying robots such as the 20-gram DelFly Explorer form an extreme challenge to artificial intelligence, because of the strict limitations in onboard sensors, processing, and memory. He tries to uncover general principles of intelligence that will allow such limited, small robots to perform complex tasks.
Guillermo Gallego works on Robotic Interactive Perception at TU Berlin as well as on computer vision and robotics. He focuses on robot perception and on optimization methods for interdisciplinary imaging and control problems. Inspired by the human visual system, he works toward improving the perception systems of artificial agents, endowing them with intelligence to transform raw sensor data into knowledge, and to provide autonomy in changing environments.
Marc Toussaint works at TU Berlin on Learning and Intelligent Systems. In his view, a key in understanding and creating intelligence is the interplay of learning and reasoning, where learning becomes the enabler for strongly generalizing reasoning and acting in our physical world. Within SCIoI, he is interested in studying computational methods and representations to enable efficient learning and general purpose physical reasoning, and demonstrating such capabilities on real-world robotic systems.
Xiaolong Wang is an Assistant Professor in the ECE department at the University of California, San Diego. He is affiliated with the CSE department, Center for Visual Computing, Contextual Robotics Institute, and the TILOS NSF AI Institute. He received his Ph.D. in Robotics at Carnegie Mellon University. His postdoctoral training was at the University of California, Berkeley. His research focuses on the intersection between computer vision and robotics. He is particularly interested in learning 3D and rich representations from videos on large-scale with minimum cost and uses this representation to guide robots to learn. He is the recipient of the NSF CAREER Award, Sony Research Award, and Amazon Research Award
Heiko Hamann works at the University of Konstanz. He is a roboticist with focus on collective systems. With his group, he studies distributed robotics, machine learning for robotics, and bio-hybrid systems. He investigates collective intelligence and especially the swarm-robotics aspects of “Speed-accuracy tradeoffs in distributed collective decision making.”
Javier Alonso-Mora lead the Autonomous Multi-Robots Laboratory at the Delft University of Technology. The goal of the Autonomous Multi-Robots Laboratory at the Delft University of Technology is to develop novel methods for navigation, motion planning, learning and control of autonomous mobile robots, with a special emphasis on multi-robot systems, on-demand transportation and robots that interact with other robots and humans in dynamic and uncertain environments. Building towards the smart cities of the future, our applications include self-driving vehicles, mobile manipulators, micro-aerial vehicles, last-mile logistics and ride-sharing.
Jorg Raisch works at TU Berlin. He represents the control discipline. His research interests include both methodological and applied aspects of control. In the context of SCIoI, his work focuses on abstraction-based synthesis of discrete event and hybrid control systems, consistent control hierarchies, and consensus-based control of multiagent systems.
Martin Saska works at the Czech Technical University of Prague. His research interests are motion planning, swarm robotics, modular robotics, and robotic simulators. During his PhD thesis he worked on “Identification, Optimization and Control with Applications in Modern Technologies.”
Sabine Hauert is Associate Professor (Reader) of Swarm Engineering at the University of Bristol in the UK. Her research focusses on making swarms for people, and across scales, from nanorobots for cancer treatment, to larger robots for environmental monitoring, or logistics. Before joining the University of Bristol, Sabine engineered swarms of nanoparticles for cancer treatment at MIT, and deployed swarms of flying robots at EPFL.
Sabine is also President and Co-founder of Robohub.org, and executive trustee of AIhub.org, two non-profits dedicated to connecting the robotics and AI communities to the public.
As an expert in science communication with 10 years of experience, Sabine is often invited to discuss the future of robotics and AI, including in the journal Nature, at the European Parliament, and at the Royal Society. Her work has been featured in mainstream media including BBC, CNN, The Guardian, The Economist, TEDx, WIRED, and New Scientist.
Shinkyu Park is the Assistant Professor of Electrical and Computer Engineering and Principal Investigator of Distributed Systems and Autonomy Group at King Abdullah University of Science and Technology (KAUST). Park’s research focuses on the learning, planning, and control in multi-agent/multi-robot systems. He aims to make foundational advances in robotics science and engineering to build individual robots’ core capabilities of sensing, actuation, and communication and to train them to learn the ability to work as a team and attain high-level of autonomy in distributed information processing, decision making, and manipulation. Prior to joining KAUST, he was Associate Research Scholar at Princeton University engaged in cross-departmental robotics projects. He received the Ph.D. degree in electrical engineering from the University of Maryland College Park in 2015. Later he held Postdoctoral Fellow positions at the National Geographic Society (2016) and Massachusetts Institute of Technology (2016-2019).
Perception and Learning in Nature
Alessio Franci works as a lecturer at the University of Liege. He is broadly interested in the interaction between mathematics, biology (particularly neuroscience), and engineering (particularly control theory and neuromorphic engineering). The brain, its way of knowing and perceiving the world, and the ways in which biology seems to self-organize out of inert matter, always fascinated him. As a physicist, he considers mathematics the natural language through which to describe the various facets of the biological world. Control theory provides both a conceptual and a technical framework to use mathematics to describe open systems, that is, systems with inputs and outputs, like our brain, like a single cell, like a group of neurons or people or bees or robots or ants; like all those biological forms in constant interaction with their environment.
Basil el Jundi works as associate professor at the Norwegian University of Science and Technology. He and his team are interested in understanding the behavioral and neural mechanisms underlying spatial orientation in insects. Currently, they are studying the use of compass cues in monarch butterflies and how they are encoded in the butterfly brain. These butterflies are famous for their spectacular annual migration from North America to Central Mexico. How are different navigation cues used for orientation and how are they linked in the brain? To understand this, we perform behavioral experiments (flight simulator) combined with anatomical (confocal imaging, 3D modeling) and electrophysiological studies (intracellular and tetrode recordings).
Iain Couzin works at the Max Planck Institute of Animal Behavior in Konstanz. He is Director of the Department of Collective Behavior (Max Planck Institute of Animal Behavior) and a Full Professor at the University of Konstanz, where he is also a spokesperson of the Cluster of Excellence ‘Centre for the Advanced Study of Collective Behaviour’. Previously he was a Full Professor in the Department of Ecology and Evolutionary Biology at Princeton University (2013), and prior to that a Royal Society University Research Fellow in the Department of Zoology, University of Oxford, and a Junior Research Fellow in the Sciences at Balliol College, Oxford (2002-2007).
Lauren Sumner-Rooney works at the Museum für Naturkunde Leibniz-Institut für Evolutions- und Biodiversitätsforschung. Her research group studies the structure, function and evolution of animal visual systems, with a focus on many-eyed organsims such as molluscs, spiders and echinoderms. The group uses a combination of digital morphology, neuroethology, evo-devo and comparative phylogenetic methods to study how and why animals use more than two eyes, and how these unusual visual systems evolve. Other research interests include the evolution of eye loss in dark habitats, the impacts of artificial light on visual ecology, and invertebrate neuroanatomy.
Mike Webster works at the University of St. Andrews. He is interested in the behaviour of group-living animals, including social foraging, competition, information diffusion and predator-prey interactions. His work investigates the benefits and costs of grouping, how groups form and function and how the behaviour of individuals shapes, and is shaped by, that of the group. He is also interested in sampling biases in animal behaviour, with a focus on how these arise and how they are reported.
Pawel Romanczuk works an HU Berlin, at the interface of applied mathematics, theoretical physics, and behavioral biology. He focuses on collective behavior of organismic systems. His research bridges analytical and synthetic sciences to study self-organization, evolutionary adaptations, and functional dynamical behavior.
List of Tutorials
Individual Robot Perception and Learning
Pia Bideau is a postdoctoral researcher at TU-Berlin and part of the Cluster Science of Intelligence as of January 2020. Her research aims to address the topic of how one can teach a computer to see and understand the world as we humans do, the strengths and weaknesses of a computer vision system compared to a human vision system, and how the two systems can learn from each other. We move, we discover new interesting stuff that raises our curiosity if a perceived situation doesn’t match certain expectations, and we learn. Pia’s research focuses on motion – our motion as well as our motion perception. Motion is a key ability that we as living beings have to explore our environment. Our motion for example helps us to perceive depth, and the motion of objects helps us to recognize these objects even if those are unknown to us. Motion in the visual world helps us understanding an unstructured environment we are living in. Before she joined the Cluster of Intelligence, Pia received her PhD from the University of Massachusetts, Amherst (USA) working with Prof. Erik Learned-Miller and worked together with Cordelia Schmid and Karteek Alahari as part of an internship at Inria in Grenoble (France).
Tutorial Title: Individual Robot Perception and Learning
Abstract: Current computer vision methods aim at processing an entire image at once. Prior work however has shown that the approach “one image at once” might not be the most effective. Instead, biological vision explores a scene while consecutively fixating on different regions in the scene. How does such active scene exploration lead to an improved understanding of objects and their relative locations?
Wolfgang Hönig is an independent junior research group leader at TU-Berlin heading the Intelligent Multi-Robot Coordination Lab. Previously, he was a postdoctoral scholar at the Department of Aerospace, California Institute of Technology, advised by Soon-Jo Chung. He holds a PhD in Computer Science from the the University of Southern California, where he was advised by Nora Ayanian. His research focuses on enabling large teams of physical robots to collaboratively solve real-world tasks, using tools from informed search, optimisation, and machine learning.
Tutorial Title: Multi-Robot Decision Making
Abstract: Intelligent behavior of a single robot is not sufficient for executing tasks with a robotic team effectively. First, we look at the challenges that arise in controls, motion planning, and general decision-making when moving from a single robot to cooperative behavior. Then, we discuss algorithms on how we can overcome these challenges, including hungarian method, buffered voronoi cells, and conflict-based search.
Perception and Learning in Nature
David Bierbach is a biologist working at Humboldt Universität zu Berlin. He is interested in topics that range from individual differences to large-scale collective behaviors. He integrates field-based studies with analytical and experimental approaches in the laboratory. Through his highly interdisciplinary work, he has developed several experimental techniques to study animal behavior in the most standardized ways, from video playbacks and computer animations to the use of bio-mimetic robots. His main research objectives are tropical freshwater fish like clonal mollies (Poecilia formosa), guppies (P. reticulata) or sulfur mollies (P. sulphuraria). At SCIoI, he is investigating how fish use anticipation in their social interactions and how information is effectively transferred within groups.
Tutorial Title: Perception and Learning in Nature
Abstract: Animals are extremely effective in processing cues from their environment. But how to design experiments that help us gaining insights into their abilities and the underlying mechanisms? Using livebearing fishes (Family Poeciliidae) as model organisms, we will explore several experimental paradigms that can inform us about how nature solved some of the problems research on AI and robotics is currently facing.