KnowRob is a knowledge processing framework designed specifically for robots. KnowRob offers a comprehensive set of tools for knowledge acquisition, representation, and reasoning. It enables robots to organize information into reusable knowledge chunks and perform reasoning using an expressive logic formalism. Furthermore, KnowRob provides visualization tools and mechanisms for knowledge acquisition, aiding in the creation of robots capable of complex manipulation tasks and interaction within dynamic environments. This system stands out for its ability to combine knowledge representation and reasoning methods with techniques for acquiring and grounding knowledge, serving as a semantic framework for integrating information from different sources and making it actionable in robots.
Clicking on the link below will take you to the KnowRob homepage. There, you have access to everything you need to get started with KnowRob, including its open-source code, step-by-step installation guides, helpful tutorials, and detailed documentation for the software framework.
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Description
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).
Publications
- KnowRob 2.0 – A 2nd Generation Knowledge Processing Framework for Cognition-enabled Robotic Agents (Michael Beetz, Daniel Beßler, Andrei Haidu, Mihai Pomarlan, Asil Kaan Bozcuoglu and Georg Bartels), In International Conference on Robotics and Automation (ICRA), 2018.
- KnowRob – A Knowledge Processing Infrastructure for Cognition-enabled Robots (Moritz Tenorth and Michael Beetz), In International Journal of Robotics Research (IJRR), volume 32, 2013.