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
  • 235 Downloads

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.

References

  1. 1.
    Autonomous learning: Summer school (2014) Available: http://www.mis.mpg.de/calendar/conferences/2014/al.html 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

Personalised recommendations