openEASE Knowledge Service Laboratory
openEASE is a cutting-edge, web-based knowledge service that leverages the KnowRob robot knowledge representation and reasoning system to offer a machine-understandable and processable platform for sharing knowledge and reasoning capabilities. It encompasses a broad spectrum of knowledge, including insights into agents (notably robots and humans), their environments (spanning objects and substances), tasks, actions, and detailed manipulation episodes involving both robots and humans. These episodes are richly documented through robot-captured images, sensor data streams, and full-body poses, providing a comprehensive understanding of interactions. The openEASE is equipped with a robust query language and advanced inference tools, enabling users to conduct semantic queries and reason about the data to extract specific information. This functionality allows robots to articulate insights about their actions, motivations, methodologies, outcomes, and observations, thereby facilitating a deeper understanding of robotic operations and interactions within their environments.
In this laboratory, you have access to openEASE, a web-based interactive platform that offers knowledge services. Through openEASE, you can choose from various knowledge bases, each representing a robotic experiment or an episode where humans demonstrate tasks to robots. To start, select a knowledge base—for instance, ”ease-2020-urobosim-fetch-and-place”—and activate it. Then, by clicking on the ”examples” button, you can choose specific knowledge queries to run on the selected experiment’s knowledge bases, facilitating a deeper understanding and interaction with the data.
Description
The information collected in the openEASE database consists of semantically labelled episodic memories. In order to make use of it, and to enhance it by additional meta information, we use and develop a set of knowledge processing tools. One of these tools is KnowRob, which is the base system for running the openEASE web console. It supplies a large set of logical Prolog predicates that allow access to, and reasoning about the information stored in the database. Furthermore, meta information enhancing tools such as a labelling tool for recorded sequences are available. In the labelling tool, sequences of recorded activity can be annotated with semantic labels of what action was performed, using which tools, and with which outcome. The collected episodic memories are uploaded to the NEEMHub.
Courses and Tutorials
- openEASE: Check out the openEASE tutorial openEASE
For more information on these courses, visit the University of Bremen's AI department page.
Publications
- Moritz Tenorth, Jan Winkler, Daniel Beßler and Michael Beetz, “Open-EASE – A Cloud-Based Knowledge Service for Autonomous Learning”, In KI – Künstliche Intelligenz, Springer Berlin Heidelberg, 2015, doi: 10.1007/s13218-015-0364-1
- Michael Beetz, Moritz Tenorth and Jan Winkler, “Open-EASE – A Knowledge Processing Service for Robots and Robotics/AI Researchers”, In IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, USA, 2015, doi: 10.1109/ICRA.2015.7139458. Finalist for the Best Cognitive Robotics Paper Award.
- Daniel Beßler, Robert Porzel, Mihai Pomarlan, Abhijit Vyas, Sebastian Höffner, Michael Beetz, Rainer Malaka and John Bateman, “Foundations of the Socio-physical Model of Activities (SOMA) for Autonomous Robotic Agents”, In Formal Ontology in Information Systems - Proceedings of the 12th International Conference, FOIS 2021, Bozen-Bolzano, Italy, September 13-16, 2021, IOS Press, 2021, doi: 10.3233/FAIA210379
- 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