Description
RoboKudo, an open-source perception framework for mobile manipulation systems, allows to flexibly generate and execute task-specific perception processes that combine multiple vision methods. The framework is based on a novel concept that we call Unstructured Information Management (UIM) on Behavior Trees (BTs), short UIMoBT, which is a mechanism to analyze unstructured data with non-linear process flows. The technical realization is done with a datastructure called Perception Pipeline Tree (PPT), which is an extension of Behavior Trees with a focus on robot perception. RoboKudo is developed to be included in a perception-action loop of a robot system. The system can state perception tasks to RoboKudo via a query-answering interface. The interface translates a perception task query such as “find a milk box in the fridge” into a specialized perception process, represented as a PPT, which contains a combination of suitable computer vision methods to fulfill the given task.
Example Video
Our overview video presents the key ideas of the RoboKudo framework and highlights demonstrations and experiments implemented in RoboKudo. The video contains sound for narration.
Software Components
- CRAM: A software toolbox for implementing autonomous robots.
- KnowRob: A knowledge processing system for robots.
- GISKARD: A framework for constraint- and optimization-based robot motion and planning control.
- ROBOKUDO: A perception framework targeted for robot manipulation tasks.
- PyCRAM: The Python 3 re-implementation of CRAM, serving as a toolbox for designing, implementing, and deploying software on autonomous robots.
Courses and Tutorials
- Integrated Intelligent Systems: Covers contemporary AI techniques in cognitive robotics.
- SUTURO - sudo tidy-up-my-room: A project where students design their own applications to run on real robots.
For more information on these courses, visit the University of Bremen's AI department page.
Authors and Contact Details
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Prof. Michael Beetz, PhD
Head of Institute
Tel: +49 421 218 64001
Email: beetz@cs.uni-bremen.de
Profile: https://ai.uni-bremen.de/team/michael_beetz
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Patrick Mania
Tel: +49 421 218 64004
Email: pmania@cs.uni-bremen.de
Profile: https://ai.uni-bremen.de/team/patrick_mania