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Abstract Understanding human activity from a video is a fundamental problem in today’s Computer Vision and Imitation Learning. The video discusses the issue of the syntax of human activity and advances the viewpoint that perceived human activity first needs to be parsed, just as in the case of language. Using these ideas then the video proposes the Ego-OMG framework. Egocentric object manipulation graphs are graphs that are extracted from a basic parsing of a video of human activity (they represent the contacts of the left and right hand with objects in the scene) and they can be used for action prediction.


Yiannis Aloimonos is a Professor of Computational Vision and Intelligence at the Department of Computer Science, University of Maryland, College Park, and the Director of the Computer Vision Laboratory at the Institute for Advanced Computer Studies (UMIACS). He is also affiliated with the Institute for Systems Research and the Neural and Cognitive Science Program. He was born in Sparta, Greece, and studied Mathematics in Athens and Computer Science at the University of Rochester, NY (PhD 1990). He is interested in Active Perception and the modeling of vision as an active, dynamic process for real-time robotic systems. For the past five years, he has been working on bridging signals and symbols, specifically on the relationship of vision to reasoning, action, and language.

Path planning is a core problem in robotics. Lydia Kavraki developed a method called the Probabilistic Roadmap Method (PRM), which caused a paradigm shift in the robotics community. The approach introduced randomization schemes that exploited local geometric properties and produced efficient solutions without fully exploring the underlying search space.

Lydia E. Kavraki is the Noah Harding Professor of Computer Science, professor of Bioengineering, professor of Electrical and Computer Engineering, and professor of Mechanical Engineering at Rice University. She is also the Director of the Ken Ken- nedy Institute at Rice. In robotics and AI, she is interested in enabling robots to work with people and in support of people.

There’s a common misconception that decisions made by computers are automatically unbiased – as opposed to those made by humans. However, Chad Jenkins pointed out many ways in which AI can fail to deliver fair and reasonable results. He pointed out what needs to be done in AI to get the intellectual domain right and how the technology and understanding researchers generate can have a positive impact on the world.

Chad Jenkins is a Professor of Computer Science and Engineering at the University of Michigan as well as the Associate Director of the Robotics Institute and the Editor-in-Chief of the journal “ACM Transactions on Human-Robot Interaction”. His research interests include mobile manipulation, computer vision, interactive robot systems, and human-robot interaction.

Jan Andersen is Head of Research Office at the University of Southern Denmark. He has a background in Computer Science and Danish Language. He has been working with research strategy and research planning. He was involved in building up four very successful research support units. He was an advisor for Rectors of the Danish Technical University, University of Copenhagen, and the former Royal Veterinary and Agricultural University. He was responsible for the cross-faculty follow-up of the Danish university merger in 2007. He was Head of the Nordic Association of University Administrators Working Group for Research Administrators. Jan Andersen is an expert on the EU framework programs. Jan Andersen was a Board Member and co-founder of the Danish Association of Research Managers and Administrators. Jan Andersen was hosting the 2009 Annual Conference of European Association of Research Managers and Administrators, EARMA and elected Chairman for EARMA 2010-2013, and board member until 2018. From 2013-2018 Jan Andersen was Chair of the COST BESTPRAC Targeted Network, with more than 650 participants from 41 countries. Jan Andersen is the co-author of “Research Management – Europe and beyond”. Presentation abstract: The changing environment of research towards “Open Science”, competitive and collaborative research projects, influences the careers of young researchers. Competition, quality and the necessity of meeting societal challenges and other “non-academic” requirements in the pursue of funding highlight the need for external advice and counseling. Here comes your local research manager and administrator to rescue. Research managers and administrators facilitate the research process from the idea to the realization of the research project. We can be a sparring partner on your career, help you identify funding, and explain – sometimes even solve the non-academic parts in research application e.g. impact, gender issues, open science, and ethics. This presentation discusses the emergence of professional support staff, and how you can benefit from involving your local research manager and administrator.

The tools available at our disposal for solving some of the really hard problems our society is facing today are old, inadequate, and need to be deprecated. Everyone is looking to Artificial Intelligence based solutions like it’s the next gold rush, without understanding how Machine Learning actually works. We’re pumping massive amounts of data into these systems, without realizing that this data came before Artificial Intelligence was a thing, and even before the Internet or computers existed: 2D images are actually just digital representations of something that was available before in analog form. Someone needs to pause, zoom out, and take a look first and foremost at the problems that we need to solve, identify and analyze them, and only then derive complete technical solutions that might or might not involve the current generation of Machine Learning en vogue, but most importantly, might indicate that new types and formats of data, whether visual or otherwise, need to be created. And the field that made significant progress there is robotics, where mapping and identifying the world with high accuracy was an absolute requirement for the stability and performance of a machine moving into our world. However, these concepts such as 3D visual representations have not yet been translated fully to scale and made de facto standards for other more common applications. In this talk, we’re taking a trip down memory lane at some of these concepts, and discussing how open source platforms such as the Point Cloud Library (PCL) have contributed to the proliferation of new visual understanding technologies. We’re also taking a look at Fyusion, which attempts to redefine the meaning of “scalable 3D visual formats”, and has created the first comprehensive and scalable technology stack for capturing photorealistic 3D spatial models of the real world using a single camera, built with Visual Understanding in mind.

Tools and Datasets

The video is a tutorial showing the basics of the CRAM framework, which is a toolbox for designing, implementing, and deploying software on autonomous robots. The aim of the tutorial is to (1) give an intuition of what knowledge the robot needs to execute even a simple fetch and place, (2) show how many different things can go wrong and teach writing simple failure handling strategies, and (3) make the user familiar with the API of the actions already implemented in the CRAM framework.


Gayane (shortly Gaya) is a PhD student at the Institute for Artificial Intelligence of the University of Bremen. Her main research interests are concentrated in the area of cognition-enabled robot executives. She is currently actively and passionately involved in the development of CRAM. Before joining Michael Beetz's group in November 2013, she worked for one year as a research assistant at Kastanienbaum GmbH (now Franka Emika) with Sami Haddadin, in tight collaboration with the Robotics and Mechatronics Center of DLR. Before that, she has acquired her M.Sc. degree in Informatics with a major in AI and Robotics at the Technical University of Munich. Before coming to Germany, she had a number of short-term jobs in the fields of iPhone game development and web development. She's got her B.Eng. degree in Informatics with a major in Information Security from the State Engineering University of Armenia.


openEASE is a web-based Knowledge Processing Service for Robots and Robotics/AI Researchers. 


pracmln is a toolbox for statistical relational learning and reasoning and as such also includes tools for standard graphical models. pracmln is a statistical relational learning and reasoning system that supports efficient learning and inference in relational domains. pracmln has started as a fork of the ProbCog toolbox and has been extended by latest developments in learning and reasoning by the Institute for Artificial Intelligence at the University of Bremen, Germany.


KnowRob is a knowledge processing system that combines knowledge representation and reasoning methods with techniques for acquiring knowledge and for grounding the knowledge in a physical system and can serve as a common semantic framework for integrating information from different sources. KnowRob combines static encyclopedic knowledge, common-sense knowledge, task descriptions, environment models, object information and information about observed actions that has been acquired from various sources (manually axiomatized, derived from observations, or imported from the web). It supports different deterministic and probabilistic reasoning mechanisms, clustering, classification and segmentation methods, and includes query interfaces as well as visualization tools.


CRAM (Cognitive Robot Abstract Machine) is a software toolbox for the design, the implementation, and the deployment of cognition-enabled autonomous robots performing everyday manipulation activities. CRAM equips autonomous robots with lightweight reasoning mechanisms that can infer control decisions rather than requiring the decisions to be preprogrammed. This way CRAM-programmed autonomous robots are much more flexible, reliable, and general than control programs that lack such cognitive capabilities. CRAM does not require the whole domain to be stated explicitly in an abstract knowledge base. Rather, it grounds symbolic expressions in the knowledge representation into the perception and actuation routines and into the essential data structures of the control programs.