Bremen University 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 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.

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.

Part 2: In his second lecture, Kei Okada discusses episodic memory. It describes the collection of past personal experiences in comparison to semantic memory that refers to the general knowledge about the world humans accumulate throughout their life. In order to achieve a goal like tidying up objects, a robot has to rely on acquired knowledge about where to find objects and what to do with them.

Part 1: In the first part of his captivating lecture, Rachid Alami discusses decisional abilities required for Human-Robot Interaction (HRI) and Human-Robot Collaboration in particular. The challenge is to develop and build cognitive and interactive abilities to allow robots to perform collaborative tasks with humans, not for humans. The first part centers on the introduction to Human-Robot Joint Actions and the problems of combining tasks planning (what to do) with motion planning (how to do it), especially for grasping, and how they can be solved.

Part 1: Kei Okada starts his first talk with a short introduction on the history of humanoid robotics research at JSK and presents various former projects such as HARP (Human Autonomous Robot Project) . He then continues to explore knowledge representation of everyday activities and knowledge-based object localization before concluding with motion imitation for robots. The compact and thorough presentation is suitable for beginners.