Skip to main content
SpringerLink
Log in
Book cover

Handbook of Anticipation pp 1–30Cite as

Anticipation in Robotics

  • Living reference work entry
  • First Online:

Abstract

In this chapter, we introduce anticipatory robotic systems. We show how intelligent robots can anticipate the future, by outlining two broad approaches: the first shows how robots can use anticipation to learn how to control their own bodies and the second shows how robots can use anticipation to predict the behavior of themselves interacting with others, and hence demonstrate improved safety, or simple ‘ethical’ behaviors. Both approaches are illustrated with experimental results from recent work. We show that, with practical realizable embedded artificial intelligence, robots can indeed predict the future and that this is a technology with significant potential for improved safety and human-robot interaction.

This is a preview of subscription content, log in via an institution.

References

  • Asimov, I. (1950). I, Robot. New York: Gnome Press.

    Google Scholar 

  • Baranes, A., & Oudeyer, P.-Y. (2013). Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems, 61, 49–73.

    Article  Google Scholar 

  • Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. Boston: MIT Press.

    Google Scholar 

  • Bechtle, S., Schillaci, G., & Hafner, V. V. (2016). On the sense of agency and of object permanence in robots. In Proceedings of the 6th joint IEEE international conference on development and learning and on epigenetic robotics, Paris, pp. 166–171.

    Google Scholar 

  • Bjerknes, J. D., & Winfield, A. F. T. (2013). On fault tolerance and scalability of swarm robotic systems, distributed autonomous robotic systems (pp. 431–444). Heidelberg: Springer.

    Book  Google Scholar 

  • Blakemore, S. J., Wolpert, D., & Frith, C. (2000). Why can’t you tickle yourself? Neuroreport, 11, 11–16.

    Article  Google Scholar 

  • Blum, C. (2015). Self-organization in networks of mobile sensor nodes. PhD thesis, Mathematisch-Naturwissenschaftliche Fakultät, Humboldt-Universität zu Berlin.

    Google Scholar 

  • Blum, C., Winfield, A. F. and Hafner, V. V. (2018). Simulation-based internal models for safer robots. Frontiers in Robotics and AI, 4, 74. https://doi.org/10.3389/frobt.2017.00074. https://www.frontiersin.org/articles/10.3389/frobt.2017.00074/abstract.

  • Bongard, J., Zykov, V., & Lipson, H. (2006). Resilient machines through continuous self-modeling. Science, 314(5802), 1118–1121.

    Article  Google Scholar 

  • Braitenberg, V. (1984). Vehicles: Experiments in synthetic psychology. Cambridge, MA: MIT Press.

    Google Scholar 

  • Brooks, R. A. (1991). Intelligence without representation. Artificial Intelligence, 47(1–3), 139–159.

    Article  Google Scholar 

  • Carpenter, M., & Call, J. (2006). The question of ‘what to imitate’: Inferring goals and intentions. In C. L. Nehaniv & D. Kirstin (Eds.), Imitation and social learning in robots, humans and animals behavioural, social and communicative dimensions (pp. 135–152). Cambridge: Cambridge University Press.

    Google Scholar 

  • Carruthers, P., & Smith, P. K. (1996). Theories of theories of mind. Cambridge: Cambridge University Press.

    Book  Google Scholar 

  • Clark, A. (2016). Surfing uncertainty – Prediction, action, and the embodied mind. New York: Oxford University Press.

    Book  Google Scholar 

  • Copete, J. L., Nagai, Y., & Asada, M. (2016). Motor development facilitates the prediction of others’ actions through sensorimotor predictive learning. In Proceedings of the 6th IEEE international conference on development and learning and on epigenetic robotics, 19–22 Sept 2016.

    Google Scholar 

  • Dautenhahn, K., & Nehaniv, C. (2002). Challenges in building robots that imitate people (pp. 363–390). Cambridge: MIT Press.

    Google Scholar 

  • Dearden, A., & Demiris, Y. (2005). Learning forward models for robots. In Proceedings of the 19th international joint conference on artificial intelligence, IJCAI’05, pp. 1440–1445.

    Google Scholar 

  • Dennett, D. (1995). Darwin’s dangerous idea. London: Penguin.

    Google Scholar 

  • Dennis, L. A., Fisher, M., Webster, M., & Bordini, R. H. (2012). Model checking agent programming languages. Automated Software Engineering, 19(1), 5–63.

    Article  Google Scholar 

  • Diamond, A., Knight, R., Devereux, D., & Holland, O. (2012). Anthropomimetic robots: Concept, construction and modelling. International Journal of Advanced Robotic Systems, 9, 209.

    Article  Google Scholar 

  • Dixon, C., Fisher, M., & Bolotov, A. (2002). Resolution in a logic of rational agency. Artificial Intelligence, 139(1), 47–89.

    Article  Google Scholar 

  • Friston, K., FitzGerald, T., Rigoli, F., Schwartenbeck, P., O’Doherty, J., & Pezzulo, G. (2016). Active inference and learning. Neuroscience and Biobehavioral Reviews, 68, 862–879.

    Article  Google Scholar 

  • Gariépy, J. F., Watson, K. K., Du, E., Xie, D. L., Erb, J., Amasino, D., & Platt, M. L. (2014). Social learning in humans and other animals. Frontiers in Neuroscience, 8, 58.

    Google Scholar 

  • Holland, J. (1992). Complex adaptive systems. Boston: Daedalus.

    Google Scholar 

  • Holland, O. (Ed.). (2003). Machine consciousness. Thorverton: Imprint Academic.

    Google Scholar 

  • Isidori, A., Marconi, L., & Serrani, A. (2003). Fundamentals of internal-model based control theory. In Robust autonomous guidance, advances in industrial control (pp. 1–58). London: Springer.

    Chapter  Google Scholar 

  • Jacobi, N., Husbands, P., & Harvey, I. (1995). Noise and the reality gap: The use of simulation in evolutionary robotics. In Proceedings of the third European conference on advances in artificial life, pp. 704–720. Springer.

    Google Scholar 

  • Jones, S., Studley, M., & Winfield, A. F. (2014). Mobile GPGPU acceleration of embodied robot simulation. In C. J. Headleand, W. J. Teahan, & L. Ap Cenydd, (Eds.) (2014), Artificial life and intelligent agents: First international symposium, ALIA 2014, Bangor, 5–6 Nov 2014. Revised Selected Papers (519), pp. 97–109. Springer.

    Google Scholar 

  • Kaplan, F., & Hafner, V. V. (2006). The challenges of joint attention. Interaction Studies, 7(2), 135–169.

    Article  Google Scholar 

  • Koenig, N., & Howard, A. (2004). Design and use paradigms for gazebo, an open-source multi-robot simulator. In Proceedings 2004 IEEE/RSJ international conference on intelligent robots and systems (IROS), Vol. 3, pp. 2149–2154. IEEE.

    Google Scholar 

  • Krahe, R., & Maler, L. (2014). Neural maps in the electrosensory system of weakly electric fish. Current Opinion in Neurobiology, 24, 13–21. https://doi.org/10.1016/j.conb.2013.08.013. ISSN 0959-4388.

    Article  Google Scholar 

  • Liu, W., & Winfield, A. F. (2011). Open-hardware e-puck Linux extension board for experimental swarm robotics research. Microprocessors and Microsystems, 35(1), 60–67.

    Article  Google Scholar 

  • Marques, H., & Holland, O. (2009). Architectures for functional imagination. Neurocomputing, 72(4–6), 743–759.

    Article  Google Scholar 

  • Marques, H. G., Jantsch, M., Wittmeier, S., Holland, O., Alessandro, C., Diamond, A., Lungarella, M., & Knight, R. (2010). ECCE1: The first of a series of anthropo-mimetic musculoskeletal upper torsos. In 10th IEEE-RAS international conference on humanoid robots, pp. 391–396. IEEE.

    Google Scholar 

  • Maturana, H. R., & Varela, F. J. (1987). The tree of knowledge: The biological roots of human understanding. Boston: New Science Library/Shambhala Publications.

    Google Scholar 

  • Michel, O. (2004). Webots: Professional Mobile Robot Simulation, International Journal of Advanced Robotic Systems, 1(1), pages 39–42.

    Google Scholar 

  • Michlmayr, M. (2002). Simulation theory versus theory theory: Theories concerning the ability to read minds. Diploma thesis, Leopold-Franzens-Universitat, Innsbruck.

    Google Scholar 

  • Millard, A. G., Timmis, J., & Winfield, A. F. T. (2013). Towards exogenous fault detection in swarm robotic systems. Towards Autonomous Robotic Systems, 429–430.

    Google Scholar 

  • Millard, A., Timmis, J., & Winfield, A. F. (2014). Run-time detection of faults in autonomous mobile robots based on the comparison of simulated and real robot behaviour. In IEEE/RSJ international conference on intelligent robots and systems (IROS 2014).

    Google Scholar 

  • Mondada, F., Bonani, M., Raemy, X., Pugh, J., Cianci, C., Klaptocz, A., Magnenat, S., Zufferey, J. C., Floreano, D., & Martinoli, A. (2009). The e-puck, a robot designed for education in engineering. In Proceedings of 9th conference on autonomous robot systems and competitions, pp. 59–65.

    Google Scholar 

  • Moore, R. K. (2012). Lecture: Extending Maturana & Varela’s symbols, FECS, Feb 2012 pp. 1–20.

    Google Scholar 

  • Moore, R. K. (2016). Introducing a pictographic language for envisioning a rich variety of enactive systems with different degrees of complexity. International Journal of Advanced Robotic Systems, 13(74), 1–20.

    Google Scholar 

  • Morse, A. F., Greef, J. D., Belpaeme, T., & Cangelosi, A. (2010). Epigenetic robotics architecture (ERA). IEEE Transactions on Autonomous Mental Development, 2, 325–339.

    Article  Google Scholar 

  • Nagai, Y., Hosoda, K., Morita, A., & Asada, M. (2003). A constructive model for the development of joint attention. Connection Science, 15(4), 211–229.

    Article  Google Scholar 

  • O’Dowd, P., Studley, M., & Winfield, A. F. (2014). The distributed co-evolution of an on-board simulator and controller for swarm robot behaviours. Evolutionary Intelligence, 7(2), 95–106.

    Article  Google Scholar 

  • Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V. (2007). Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation, 11, 265–286.

    Article  Google Scholar 

  • Pico, A., Schillaci, G., Hafner, V. V., & Lara, B. (2016). How do I sound like? Forward models for robot ego-noise prediction. In Proceedings of the 6th joint IEEE international conference on development and learning and on epigenetic robotics, pp. 246–251. Paris.

    Google Scholar 

  • Rolf, M., & Steil, J. J. (2014). Explorative learning of inverse models: A theoretical perspective. Neurocomputing, 131, 2–14.

    Article  Google Scholar 

  • Rosen, R. (1985). Anticipatory systems: Philosophical, mathematical and methodological foundations. Oxford: Pergamon Press.

    Google Scholar 

  • Schillaci, G. (2014). Sensorimotor learning and simulation of experience as a basis for the development of cognition in robotics. Dissertation, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II. https://doi.org/10.18452/16920.

  • Schillaci, G., & Hafner, V. V. (2011). Random movement strategies in self-exploration for a humanoid robot. In 6th ACM/IEEE international conference on human-robot interaction (HRI), pp. 245–246, Lausanne.

    Google Scholar 

  • Schillaci, G., Hafner, V. V., & Lara, B. (2012a). Coupled inverse-forward models for action execution leading to tool-use in a humanoid robot. In Proceedings of the 7th ACM/IEEE international conference on human-robot interaction (HRI 2012), pp. 231–232, Boston.

    Google Scholar 

  • Schillaci, G., Lara, B., & Hafner, V. V. (2012b). Internal simulations for behaviour selection and recognition. In A. A. Salah, et al. (Eds.), Human behaviour understanding 2012 (Lecture Notes in Computer Science, Berlin, Heidelberg: Springer, Vol. 7559, pp. 148–160).

    Google Scholar 

  • Schillaci, G., Ritter, C. -N., Hafner, V. V., & Lara, B. (2016a). Body representations for robot ego-noise modelling and prediction. Towards the development of a sense of agency in artificial agents. In International conference on the simulation and synthesis of living systems (ALife XV), pp. 390–397, Mexico: MIT Press, July 2016.

    Google Scholar 

  • Schillaci, G., Hafner, V. V., & Lara, B. (2016b). Exploration behaviours, body representations and simulations processes for the development of cognition in artificial agents. Frontiers in Robotics and AI, Section Humanoid Robotics, 3, 39. https://doi.org/10.3389/frobt.2016.00039.

    Google Scholar 

  • Schillaci, G., Hafner, V. V., & Lara, B. (Eds.). (2016c). Re-enacting sensorimotor experience for cognition. Research Topic in Frontiers in Robotics and AI, Section Humanoid Robotics. https://doi.org/10.3389/frobt.2016.00077.

  • Silver, D., et al. (2016). Mastering the game of go with deep neural networks and tree search. Nature, 529, 484–489. https://doi.org/10.1038/nature16961.

    Article  Google Scholar 

  • Vanderelst, D., & Winfield, A. (2017). Rational imitation for robots: The cost difference model. Adaptive Behavior, 25(2), 60–71.

    Article  Google Scholar 

  • Vaughan, R. T., & Zuluaga, M. (2006). Use your illusion: Sensorimotor self-simulation allows complex agents to plan with incomplete self-knowledge. In Proceedings of international conference on simulation of adaptive behaviour (SAB), pp. 298–309.

    Google Scholar 

  • Vaughan R.T., & Gerkey B.P. (2007). Reusable Robot Software and the Player/Stage Project. In: Brugali D. (eds) Software Engineering for Experimental Robotics. Springer Tracts in Advanced Robotics, vol 30. Springer, Berlin, Heidelberg.

    Google Scholar 

  • Vernon, D., Beetz, M., & Sandini, G. (2015). Prospection in cognition: The case for joint episodic-procedural memory in cognitive robotics. Frontiers in Robotics and AI, Section Humanoid Robotics, 2, 19.

    Google Scholar 

  • Winfield, A. F. (2012). Robotics: A very short introduction. New York: Oxford University Press.

    Book  Google Scholar 

  • Winfield A.F.T., & Jirotka M. (2017). The Case for an Ethical Black Box. In: Gao Y., Fallah S., Jin Y., Lekakou C. (eds) Towards Autonomous Robotic Systems. TAROS 2017. Lecture Notes in Computer Science, vol 10454. Springer, Cham.

    Google Scholar 

  • Winfield, A. F. (2018). When robots tell each other stories: The emergence of artificial fiction. In S. Stepney & R. Walsh (Eds.), Narrating complexity. Springer International Publishing, in press.

    Google Scholar 

  • Winfield, A. F., Blum, C., & Liu, W. (2014). Towards an ethical robot: Internal models, consequences and ethical action selection. In M. Mistry, A. Leonardis, M. Witkowski, & C. Melhuish (Eds.), Advances in autonomous robotics systems (pp. 85–96). Cham: Springer.

    Google Scholar 

  • Wolpert, D. M., Ghahramani, Z., & Jordan, M. I. (1995). An internal model for sensorimotor integration. Science, 269, 1880.

    Article  Google Scholar 

  • Wolpert, D. M., Goodbody, S. J., & Husain, M. (1998). Maintaining internal representations: The role of the human superior parietal lobe. Nature Neuroscience, 1, 529–533.

    Article  Google Scholar 

  • Zagal, J. C., Delpiano, J., & Ruiz-del Solar, J. (2009). Self-modeling in humanoid soccer robots. Robotics and Autonomous Systems, 57(8), 819–827.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Bristol Robotics Laboratory, University of the West of England, Coldharbour Lane, Bristol, BS16 1QY, UK

    Alan F. T. Winfield

  2. Department of Computer Science, Humboldt-Universität zu Berlin, Unter den Linden 6, 10099, Berlin, Germany

    Verena V. Hafner

Authors
  1. Alan F. T. Winfield
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Verena V. Hafner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alan F. T. Winfield .

Editor information

Editors and Affiliations

  1. Dept of Sociology & Social Research, University of Trento Dept of Sociology & Social Research, Trento, Italy

    Roberto Poli

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this entry

Cite this entry

Winfield, A.F.T., Hafner, V.V. (2018). Anticipation in Robotics. In: Poli, R. (eds) Handbook of Anticipation. Springer, Cham. https://doi.org/10.1007/978-3-319-31737-3_73-1

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-31737-3_73-1

  • Received:

  • Accepted:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31737-3

  • Online ISBN: 978-3-319-31737-3

  • eBook Packages: Springer Reference Religion and PhilosophyReference Module Humanities and Social SciencesReference Module Humanities

Publish with us

Policies and ethics

Search

Navigation