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Bremen University students

Special content for students of the Bremen University

Data for Robot Manipulation

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.

Task Instantiation from Life-long Memories of Mobile Robots

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.

On Decisional Abilities for a Cognitive and Interactive Robot

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.

Humanoid Robots in Everyday Activities

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.

Digital Twin Knowledge Bases

Follow Michael Beetz' talk on the exciting topic of digital twin knowledge bases. The term digital twins refers to virtual, AI-based images of physical objects in the real world. It is an emerging technology and plays a crucial role for the Industry 4.0 and the digitization of manufacturing in several domains. In retail, for example, digital twins show an exact digital replica of the store and warehouse and the location of each product. In his comprehensive talk, Michael Beetz focuses on the aspect of knowledge representation.


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).


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.


RoboSherlock is a common framework for cognitive perception, based on the principle of unstructured information management (UIM). UIM has proven itself to be a powerful paradigm for scaling intelligent information and question answering systems towards real-world complexity.