PhD students

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.

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.

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.

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.

In this talk, I review the Soar cognitive architecture, including the motivation for cognitive architecture, the history of Soar, the applications it has been applied to, and our current research on Interactive Task Learning. I then discuss Soar from the standpoint of an open-source research project: positives, negatives, and challenges.

In his captivating lecture, Frank Guerin examines the question about the integration between robot vision and a deeper understanding of tasks. As robot grasping is still an unsolved problem, he explores why and how human perception of objects is relevant to manipulation and explains what "transferrable toddler skills" are. The lecture is suitable for beginners.

From small to complex, from robot vacuum cleaner to self-driving car: every robotic system needs some sort of perceptual capabilities in order to perceive information from its environment and to understand how it can manipulate it. Perception can come in many forms. Tim Patten gives a highly interesting introduction on how robots deal with object identification: what is it? (recognition), what type is it? (classification), where is it? (object detection), and how do I manipulate it? (grasping). The talk is suitable for beginners.

Part 2: In his equally interesting follow-up lecture, Animesh Garg continues to explore compositional planning and multi-step reasoning, i.e. when a robot is supposed to do multiple tasks in a certain structure. He also examines robot perception via structured learning through instruction videos, and tackles the question of how to collect the data required for robot learning.

Michael Beetz provides an educational introduction to CRAM, Cognitive Robot Abstract Machine. How can we write a robot control program where the robot receives instructions for the performance of a task and is able to produce the behavior neccessary to accomplish the task? This simple question is not fully answered yet as there is still an information gab between instruction and body motion that has to be filled in a semantically meaningful manner. One way is simplifying perception tasks and implementing motion contrains. Follow Michael Beetz's interesting approach to metacognition.

Part 1: In his first noteworthy lecture, Animesh Garg presents his vision of building intelligent robotic assistants that learn with the same amount of efficiency and generality than humans do through learning algorithms, particularly in robot manipulation. Humans learn through instruction or imitation and can adapt to new situations by drawing from experience. The goal is to have robotic systems recognize new objects in new environments autonomously (diversity) and enable them to do things they were not trained to do by using long-term reasoning (complexity).