Episodic Memories for Safety-Aware Robots

Knowledge Representation and Reasoning for Robots that Safely Interact with Human Co-Workers
  • Georg BartelsEmail author
  • Daniel Beßler
  • Michael Beetz
Technical Contribution


In the factories and distribution centers of the future, humans and robots shall work together in close proximity and even physically interact. This shift to joint human–robot teams raises the question of how to ensure worker safety. In this manuscript, we present a novel episodic memory system for safety-aware robots. Using this system, the robots can answer questions about their actions at the level of safety concepts. We built this system as an extension of the KnowRob framework and its notion of episodic memories. We evaluated the system in a safe physical human–robot interaction (pHRI) experiment, in which a robot had to sort surgical instruments while also ensuring the safety of its human co-workers. Our experimental results show the efficacy of the system to act as a robot’s belief state for online reasoning, as well as its ability to support offline safety analysis through its fast and flexible query interface. To this end, we demonstrate the system’s ability to reconstruct its geometric environment, course of action, and motion parameters from descriptions of safety-relevant events. We also show-case the system’s capability to conduct statistical analysis.


Robot safety Knowledge representation Cognitive human–robot interaction 



We gratefully acknowledge that this work was partially funded by the FP7 Project SAPHARI (Project ID: 287513) and by Deutsche Forschungsgemeinschaft (DFG) through the Collaborative Research Center 1320 EASE.


  1. 1.
    Albu-Schäffer A, Ott C, Hirzinger G (2007) A unified passivity-based control framework for position, torque and impedance control of flexible joint robots. Int J Robot Res 26:23–39CrossRefzbMATHGoogle Scholar
  2. 2.
    Badri A, Boudreau-Trudel B, Souissi AS (2018) Occupational health and safety in the industry 4.0 era: A cause for major concern? Safety Sci 109:403–411CrossRefGoogle Scholar
  3. 3.
    Beetz M, Mösenlechner L, Tenorth M (2010) Cram—a cognitive robot abstract machine for everyday manipulation in human environments. In: International conference on intelligent robots and systems IEEE, pp 1012–1017Google Scholar
  4. 4.
    Beetz M, Jain D, Mosenlechner L, Tenorth M, Kunze L, Blodow N, Pangercic D (2012) Cognition-enabled autonomous robot control for the realization of home chore task intelligence. Proc IEEE 100:2454–2471CrossRefGoogle Scholar
  5. 5.
    Beetz M, Bálint-Benczédi F, Blodow N, Nyga D, Wiedemeyer T, Marton ZC (2015a) Robosherlock: Unstructured information processing for robot perception. In: International conference on robotics and automation IEEE, pp 1549–1556Google Scholar
  6. 6.
    Beetz M, Bartels G, Albu-Schäffer A, Bálint-Benczédi F, Belder R, Beßler D, Haddadin S, Maldonado A, Mansfeld N, Wiedemeyer T, et al (2015b) Robotic agents capable of natural and safe physical interaction with human co-workers. In: International conference on intelligent robots and systems IEEE, pp 6528–6535Google Scholar
  7. 7.
    Beetz M, Tenorth M, Winkler J (2015c) Open-ease. In: International conference on robotics and automation IEEE, pp 1983–1990Google Scholar
  8. 8.
    Beetz M, Beßler D, Haidu A, Pomarlan M, Bozcuoglu AK, Bartels G (2018) Knowrob 2.0 – a 2nd generation knowledge processing framework for cognition-enabled robotic agents. In: International conference on robotics and automation IEEE, pp 512–519Google Scholar
  9. 9.
    Ersen M, Oztop E, Sariel S (2017) Cognition-enabled robot manipulation in human environments: requirements, recent work, and open problems. Robot Autom Mag 24:108–122CrossRefGoogle Scholar
  10. 10.
    OWL Working Group et al (2009) OWL 2 web ontology language document overview: W3C recommendation 27 October 2009.
  11. 11.
    Haddadin S, Croft E (2016) Physical human–robot interaction. In: Springer handbook of robotics. Springer, New York, pp 1835–1874Google Scholar
  12. 12.
    Haddadin S, Albu-Schaffer A, De Luca A, Hirzinger G (2008) Collision detection and reaction: A contribution to safe physical human–robot interaction. In: International conference on intelligent robots and systems IEEE, pp 3356–3363Google Scholar
  13. 13.
    Hägele M, Schaaf W, Helms E (2002) Robot assistants at manual workplaces: effective co-operation and safety aspects. In: Internation Symposium on Robotics, vol 7Google Scholar
  14. 14.
    Hirzinger G, Sporer N, Albu-Schaffer A, Hahnle M, Krenn R, Pascucci A, Schedl M (2002) Dlr’s torque-controlled light weight robot iii-are we reaching the technological limits now? In: International conference on robotics and automation IEEE, vol 2, pp 1710–1716Google Scholar
  15. 15.
    Kunze L, Roehm T, Beetz M (2011) Towards semantic robot description languages. In: International conference on robotics and automation IEEE, pp 5589–5595Google Scholar
  16. 16.
    Lasota PA, Fong T, Shah JA et al (2017) A survey of methods for safe human–robot interaction. Found Trends Robot 5:261–349CrossRefGoogle Scholar
  17. 17.
    Lemaignan S, Ros R, Mösenlechner L, Alami R, Beetz M (2010) Oro, a knowledge management platform for cognitive architectures in robotics. In: International conference on intelligent robots and systems,IEEE, pp 3548–3553Google Scholar
  18. 18.
    Matthias B, Kock S, Jerregard H, Källman M, Lundberg I (2011) Safety of collaborative industrial robots: Certification possibilities for a collaborative assembly robot concept. In: International symposium on assembly and manufacturing IEEE, pp 1–6Google Scholar
  19. 19.
    Parusel S, Haddadin S, Albu-Schäffer A (2011) Modular state-based behavior control for safe human–robot interaction: A lightweight control architecture for a lightweight robot. In: International conference on robotics and automation IEEE, pp 4298–4305Google Scholar
  20. 20.
    Quigley M, Conley K, Gerkey B, Faust J, Foote T, Leibs J, Wheeler R, Ng AY (2009) Ros: an open-source robot operating system. In: ICRA workshop on open source software, vol 3, pp 5–10Google Scholar
  21. 21.
    Ragaglia M, Zanchettin AM, Rocco P (2018) Trajectory generation algorithm for safe human–robot collaboration based on multiple depth sensor measurements. Mechatronics 55:267–281CrossRefGoogle Scholar
  22. 22.
    Tenorth M, Beetz M (2012) A unified representation for reasoning about robot actions, processes, and their effects on objects. In: International conference on intelligent robots and systems IEEE, pp 1351–1358Google Scholar
  23. 23.
    Tenorth M, Beetz M (2013) Knowrob: a knowledge processing infrastructure for cognition-enabled robots. Int J Robot Res 32:566–590CrossRefGoogle Scholar
  24. 24.
    Wielemaker J, Schrijvers T, Triska M, Lager T (2012) Swi-prolog. Theory Pract Logic Program 12:67–96MathSciNetCrossRefzbMATHGoogle Scholar
  25. 25.
    Winkler J, Tenorth M, Bozcuoglu A, Beetz M (2014) Cramm-memories for robots performing everyday manipulation activities. Adv Cogn Syst 3:47–66Google Scholar
  26. 26.
    Zanchettin AM, Ceriani NM, Rocco P, Ding H, Matthias B (2016) Safety in human–robot collaborative manufacturing environments: Metrics and control. Trans Autom Sci Eng 13:882–893CrossRefGoogle Scholar

Copyright information

© Gesellschaft für Informatik e.V. and Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute for Artificial Intelligence (IAI)University of BremenBremenGermany

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