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Ontological Component-based Description of Robot Capabilities (2306.07569v2)

Published 13 Jun 2023 in cs.RO

Abstract: A key aspect of a robot's knowledge base is self-awareness about what it is capable of doing. It allows to define which tasks it can be assigned to and which it cannot. We will refer to this knowledge as the Capability concept. As capabilities stems from the components the robot owns, they can be linked together. In this work, we hypothesize that this concept can be inferred from the components rather than merely linked to them. Therefore, we introduce an ontological means of inferring the agent's capabilities based on the components it owns as well as low-level capabilities. This inference allows the agent to acknowledge what it is able to do in a responsive way and it is generalizable to external entities the agent can carry for example. To initiate an action, the robot needs to link its capabilities with external entities. To do so, it needs to infer affordance relations from its capabilities as well as the external entity's dispositions. This work is part of a broader effort to integrate social affordances into a Human-Robot collaboration context and is an extension of an already existing ontology.

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References (25)
  1. J. J. Gibson, “The ecological approach to visual perception: classic edition,” 2014.
  2. D. A. Norman, “The design of everyday things,” 2002.
  3. M. T. Turvey, “Affordances and prospective control: An outline of the ontology,” 1992.
  4. A. Chemero, “An outline of a theory of affordances,” 2003.
  5. T. A. Stoffregen, “Affordances as properties of the animal-environment system,” 2003.
  6. E. Şahin, M. Cakmak, M. R. Doğar, E. Uğur, and G. Üçoluk, “To afford or not to afford: A new formalization of affordances toward affordance-based robot control,” 2007.
  7. M. Steedman, “Formalizing affordance,” 2002.
  8. L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor, “Affordances, development and imitation,” IEEE, 2007.
  9. F. Cruz, S. Magg, C. Weber, and S. Wermter, “Training agents with interactive reinforcement learning and contextual affordances,” 2016.
  10. C. Barck-Holst, M. Ralph, F. Holmar, and D. Kragic, “Learning grasping affordance using probabilistic and ontological approaches,” IEEE, 2009.
  11. D. Beßler, R. Porzel, M. Pomarlan, M. Beetz, R. Malaka, and J. Bateman, “A formal model of affordances for flexible robotic task execution,” 2020.
  12. E. Carvalho, “Social affordance,” 2020.
  13. A. Olivares-Alarcos, D. Bessler, A. Khamis, P. Goncalves, M. K. Habib, J. Bermejo-Alonso, M. Barreto, M. Diab, J. Rosell, J. Quintas et al., “A review and comparison of ontology-based approaches to robot autonomy (vol 34, e29, 2019),” KNOWLEDGE ENGINEERING REVIEW, vol. 35, 2020.
  14. E. Prestes, J. L. Carbonera, S. R. Fiorini, V. A. Jorge, M. Abel, R. Madhavan, A. Locoro, P. Goncalves, M. E. Barreto, M. Habib et al., “Towards a core ontology for robotics and automation,” Robotics and Autonomous Systems, vol. 61, no. 11, pp. 1193–1204, 2013.
  15. G. Sarthou, A. Mayima, G. Buisan, K. Belhassein, and A. Clodic, “The director task: a psychology-inspired task to assess cognitive and interactive robot architectures,” in International Conference on Robot & Human Interactive Communication (RO-MAN).   IEEE, 2021.
  16. A. Umbrico, A. Orlandini, and A. Cesta, “An ontology for human-robot collaboration,” Procedia CIRP, 2020.
  17. M. Beetz, D. Beßler, A. Haidu, M. Pomarlan, A. K. Bozcuoğlu, and G. Bartels, “Know rob 2.0—a 2nd generation knowledge processing framework for cognition-enabled robotic agents,” in International Conference on Robotics and Automation (ICRA).   IEEE, 2018.
  18. M. Diab, A. Akbari, M. Ud Din, and J. Rosell, “Pmk—a knowledge processing framework for autonomous robotics perception and manipulation,” Sensors, 2019.
  19. M. Stenmark and J. Malec, “Knowledge-based instruction of manipulation tasks for industrial robotics,” Robotics and Computer-Integrated Manufacturing, 2015.
  20. I. Tiddi, E. Bastianelli, G. Bardaro, M. d’Aquin, and E. Motta, “An ontology-based approach to improve the accessibility of ros-based robotic systems,” 2017.
  21. D. Beßler, R. Porzel, M. Pomarlan, A. Vyas, S. Höffner, M. Beetz, R. Malaka, and J. Bateman, “Foundations of the socio-physical model of activities (soma) for autonomous robotic agents,” arXiv preprint arXiv:2011.11972, 2020.
  22. L. Kunze, T. Roehm, and M. Beetz, “Towards semantic robot description languages,” in International Conference on Robotics and Automation.   IEEE, 2011.
  23. J. Buehler and M. Pagnucco, “A framework for task planning in heterogeneous multi robot systems based on robot capabilities,” 2014.
  24. F. Amigoni and M. Neri, “An application of ontology technologies to robotic agents,” 2005.
  25. N. Krüger, C. Geib, J. Piater, R. Petrick, M. Steedman, F. Wörgötter, A. Ude, T. Asfour, D. Kraft, D. Omrčen et al., “Object-action complexes: Grounded abstractions of sensory–motor processes,” 2011.
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