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


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.


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.



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).


  1. 1.
    Autonomous learning: Summer school (2014) Available: Accessed 2015 Feb 15 [Online]
  2. 2.
    Beetz M, Tenorth M, Winkler J (2015) Open-EASE—a knowledge processing service for robots and robotics/ai researchers. In: IEEE International Conference on Robotics and Automation (ICRA), Seattle, Washington, USA (accepted for publication)Google Scholar
  3. 3.
    Winkler J, Tenorth M, Bozcuoglu AK, Beetz M (2014) CRAMm-memories for robots performing everyday manipulation activities. Adv Cogn Syst 3:47–66Google Scholar
  4. 4.
    Tenorth M, Beetz M (2013) KnowRob-a knowledge processing infrastructure for cognition-enabled robots. Int J Robot Res (IJRR) 32(5):566–590CrossRefGoogle Scholar
  5. 5.
    Ceriani S, Fontana G, Giusti A, Marzorati D, Matteucci M, Migliore D, Rizzi D, Sorrenti DG, Taddei P (2009) Rawseeds ground truth collection systems for indoor self-localization and mapping. Auton Robots 27(4):353–371CrossRefGoogle Scholar
  6. 6.
    Tenorth M, Bandouch J, Beetz M (2009) The TUM kitchen data set of everyday manipulation activities for motion tracking and action recognition. In: IEEE International Workshop on Tracking humans for the evaluation of their motion in image sequences (THEMIS), in conjunction with ICCV2009Google Scholar
  7. 7.
    Rohrbach M, Amin S, Andriluka M, Schiele B (2012) A database for fine grained activity detection of cooking activities. In: 2012 IEEE Conference on Computer vision and pattern recognition (CVPR), Providence, United StatesGoogle Scholar
  8. 8.
    De la Torre F, Hodgins J, Montano J, Valcarcel S, Macey J (2009) Guide to the Carnegie Mellon University Multimodal Activity (CMU-MMAC) Database. CMU-RI-TR-08-22, Robotics Institute. Carnegie Mellon University, Tech. RepGoogle Scholar
  9. 9.
    Beetz M, Mösenlechner L, Tenorth M (2012) CRAM-a cognitive robot abstract machine for everyday manipulation in human environments. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, Taipei, Taiwan, 18–22 Oct 2010, pp 1012–1017Google Scholar

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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