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KI - Künstliche Intelligenz

, Volume 29, Issue 4, pp 407–411 | Cite as

Open-EASE: A Cloud-Based Knowledge Service for Autonomous Learning

  • Moritz Tenorth
  • Jan Winkler
  • Daniel Beßler
  • Michael Beetz
Research Project

Abstract

We present Open-EASE, a cloud-based knowledge base of robot experience data that can serve as episodic memory, providing a robot with comprehensive information for autonomously learning manipulation tasks. Open-EASE combines both robot and human activity data in a common, semantically annotated knowledge base, including robot poses, object information, environment models, the robot’s intentions and beliefs, as well as information about the actions that have been performed. A powerful query language and inference tools support reasoning about the data and retrieving information based on semantic queries. In this paper, we focus on applications of Open-EASE in the context of autonomous learning.

Keywords

Episodic Memory Pane Query Language Image Pane Robotic Agent 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported in part by the DFG Project BayCogRob within the DFG Priority Programme 1527 for Autonomous Learning and the EU FP7 Projects RoboHow (Grant Agreement Number 288533) and SAPHARI (Grant Agreement Number 287513).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Moritz Tenorth
    • 1
  • Jan Winkler
    • 1
  • Daniel Beßler
    • 1
  • Michael Beetz
    • 1
  1. 1.Institute for Artificial IntelligenceUniversität BremenBremenGermany

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