International Journal of Social Robotics

, Volume 10, Issue 1, pp 131–145 | Cite as

A Decision-Theoretic Approach for the Collaborative Control of a Smart Wheelchair

  • Mahmoud Ghorbel
  • Joelle Pineau
  • Richard Gourdeau
  • Shervin Javdani
  • Siddhartha Srinivasa


While assistive robot technology is quickly progressing, several challenges remain to make this technology truly usable and useful for humans. One of the aspects that is particularly important is in defining control protocols that allow both the human and the robot technology to contribute to the best of their abilities. In this paper we propose a framework for the collaborative control of a smart wheelchair designed for individuals with mobility impairments. Our approach is based on a decision-theoretic model of control, and accepts commands from both the human user and robot controller. We use a Partially Observable Markov Decision Process to optimize the collaborative action choice, which allows the system to take into account uncertainty in the user intent, in the command and in the environment. The system is deployed and validated on the SmartWheeler platform, and experiments with 8 users show the improvement in usability and navigation efficiency that are achieved with this form of collaborative control.


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Funding for this project was provided through the NSERC Canadian Field Robotics Network (NCFRN), the NSERC Discovery program and the AGE-WELL NCE. Many thanks to Martin Gerdzhev, Alan Do-Omri, Anne-Marie Hébert and Dahlia Kairy for helpful suggestions throughout the experimental phase of the work.


  1. 1.
    Bonarini A, Ceriani S, Fontana G, Matteucci M (2012) Introducing lurch: a shared autonomy robotic wheelchair with multimodal interfaces. In: Proceedings of IROS 2012 workshop on progress, challenges and future perspectives in navigation and manipulation assistance for robotic wheelchairsGoogle Scholar
  2. 2.
    Cortes U, Barrue C, Martinez AB, Urdiales C, Campana F, Annicchiarico R, Caltagirone C (2010) Assistive technologies for the new generation of senior citizens: the share-it approach. Int J Comput Healthc 1(1):35–65CrossRefGoogle Scholar
  3. 3.
    Crandall JW, Goodrich MA (2002) Characterizing efficiency of human robot interaction: a case study of shared-control teleoperation. In: Intelligent robots and systems, 2002. IEEE/RSJ international conference on, vol 2, pp 1290–1295. IEEEGoogle Scholar
  4. 4.
    Demeester E, Nuttin M, Vanhooydonck D, Van Brussel H (2003) Assessing the user’s intent using bayes’ rule: application to wheelchair control. In: Proceedings of the 1st international workshop on advanced in service robotics, pp 117–124Google Scholar
  5. 5.
    Dragan AD, Srinivasa SS (2013) A policy-blending formalism for shared control. Int J Robot Res 32(7):790–805CrossRefGoogle Scholar
  6. 6.
    Fehr L, Langbein WE, Skaar SB (2000) Adequacy of power wheelchair control interfaces for persons with severe disabilities: a clinical survey. J Rehabil Res Dev 37:353Google Scholar
  7. 7.
    Goodrich MA, Boer ER, Crandall JW, Ricks RW, Quigley ML (2004) Behavioral entropy in human-robot interaction. In: Technical report DTIC DocumentGoogle Scholar
  8. 8.
    Javdani S, Bagnell JA, Srinivasa S (2015) Shared autonomy via hindsight optimization. arXiv preprint arXiv:1503.07619
  9. 9.
    Kaelbling LP, Littman ML, Cassandra AR (1998) Planning and acting in partially observable stochastic domains. Artif Intell 101:99MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Karami AB, Jeanpierre L, Mouaddib AI (2009) Partially observable markov decision process for managing robot collaboration with human. In: 2009. ICTAI’09. 21st IEEE international conference on tools with artificial intelligence , pp 518–521Google Scholar
  11. 11.
    Kim DJ, Hazlett-Knudsen R, Culver-Godfrey H, Rucks G, Cunningham T, Portee D, Bricout J, Wang Z, Behal A (2012) How autonomy impacts performance and satisfaction: results from a study with spinal cord injured subjects using an assistive robot. IEEE Trans Syst Man Cybern Part A 42:2CrossRefGoogle Scholar
  12. 12.
    Matignon L, Karami AB, Mouaddib AI (2010) A model for verbal and non-verbal human-robot collaboration. In: 2010 AAAI fall symposium seriesGoogle Scholar
  13. 13.
    Nakayama O, Futami T, Nakamura T, Boer ER (1999) Development of a steering entropy method for evaluating driver workload. SAE Trans 108((6; PART 1)):1686–1695Google Scholar
  14. 14.
    Nuttin M, Vanhooydonck D, Demeester E, Van Brussel H (2002) Selection of suitable human–robot interaction techniques for intelligent wheelchairs. In: 2002. IEEE 11th IEEE International Workshop on Robot and Human Interactive Communication. Proceedings, pp 146–151Google Scholar
  15. 15.
    Pineau J, Gordon G, Thrun S et al (2003) Point-based value iteration: an anytime algorithm for pomdps. IJCAI 3:1025–1032Google Scholar
  16. 16.
    Pineau J, West R, Atrash A, Villemure J, Routhier F (2011) On the feasibility of using a standardized test for evaluating a speech-controlled smart wheelchair. Int J Intell Control Syst 16(2):124–131Google Scholar
  17. 17.
    Simpson RC (2005) Smart wheelchairs: a literature review. J Rehabil Res Dev 42(4):423CrossRefGoogle Scholar
  18. 18.
    Spaan MT, Spaan MT (2004) A point-based pomdp algorithm for robot planning. In: 2004 IEEE international conference on robotics and automation. Proceedings. ICRA’04 ,vol 3, pp 2399–2404Google Scholar
  19. 19.
    Taha T, Miró JV, Dissanayake G (2008) Pomdp-based long-term user intention prediction for wheelchair navigation. In: IEEE international conference on robotics and automation, 2008. ICRA 2008, pp 3920–3925Google Scholar
  20. 20.
    Taha T, Miró JV, Dissanayake G (2011) A pomdp framework for modelling human interaction with assistive robots. In: 2011 IEEE international conference on robotics and automation (ICRA) , pp 544–549Google Scholar
  21. 21.
    Thrun S (2000) Probabilistic algorithms in robotics. Ai Mag 21(4):93Google Scholar
  22. 22.
    Vanhooydonck D, Demeester E, Nuttin M, Van Brussel H (2003) Shared control for intelligent wheelchairs: an implicit estimation of the user intention. In: Proceedings of the 1st international workshop on advances in service robotics (ASER03), pp. 176–182. CiteseerGoogle Scholar
  23. 23.
    Yoon SW, Fern A, Givan R, Kambhampati S (2008) Probabilistic planning via determinization in hindsight. In: AAAI, pp. 1010–1016Google Scholar
  24. 24.
    Ziebart BD, Maas AL, Bagnell JA, Dey AK (2008) Maximum entropy inverse reinforcement learning. In: AAAI, pp. 1433–1438Google Scholar
  25. 25.
    Ziebart BD, Ratliff N, Gallagher G, Mertz C, Peterson K, Bagnell JA, Hebert M, Dey AK, Srinivasa S (2009) Planning-based prediction for pedestrians. In: 2009. IEEE/RSJ international conference on intelligent robots and systems , pp 3931–3936Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.Department of Electrical EngineeringPolytechnique MontrealMontrealCanada
  2. 2.School of Computer ScienceMcGill UniversityMontrealCanada
  3. 3.Carnegie Mellon UniversityPittsburghUSA

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