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.
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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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Tenorth, M., Winkler, J., Beßler, D. et al. Open-EASE: A Cloud-Based Knowledge Service for Autonomous Learning. Künstl Intell 29, 407–411 (2015). https://doi.org/10.1007/s13218-015-0364-1
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DOI: https://doi.org/10.1007/s13218-015-0364-1