RAMCIP Robot: A Personal Robotic Assistant; Demonstration of a Complete Framework

  • Ioannis KostavelisEmail author
  • Dimitrios Giakoumis
  • Georgia Peleka
  • Andreas Kargakos
  • Evangelos Skartados
  • Manolis Vasileiadis
  • Dimitrios Tzovaras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11134)


At the last decades, personal domestic robots are considered as the future for tackling the societal challenge inherent in the growing elderly population. Ageing is typically associated with physical and cognitive decline, altering the way an older person moves around the house, manipulates objects and senses the home environment. This paper aims to demonstrate the RAMCIP robot, which is a Robotic Assistant for patients with Mild Cognitive Impairments (MCI), suitable to provide its services in domestic environments. The use cases that the robot addresses are described herein outlining the necessary requirements that set the basis for the software and hardware architectural components. A short description of the integrated cognitive, perception, manipulation and navigation capabilities of the robot is provided. Robot’s autonomy is enabled through a specific decision making and task planning framework. The robot has been evaluated in ten real home environments of real MCI users exhibiting remarkable performance.


Robotic Assistant Integrated framework Manipulation Navigation Perception Task planning Decision making MCI 



This work has been supported by the EU Horizon 2020 funded project namely: Robotic Assistant for MCI Patients at home (RAMCIP) under the grant agreement with no: 643433. The robotic platform with the arm manipulator has been developed by ACCREA Engineering and the robotic hand has been developed by Shadow Robot Company. Pilot trials have organized by ACE and LUM.


  1. 1.
    Agrigoroaie, R., Ferland, F., Tapus, A.: The ENRICHME project: lessons learnt from a first interaction with the elderly. In: Agah, A., Cabibihan, J.-J., Howard, A.M., Salichs, M.A., He, H. (eds.) ICSR 2016. LNCS (LNAI), vol. 9979, pp. 735–745. Springer, Cham (2016). Scholar
  2. 2.
    Agriomallos, I., Doltsinis, S., Mitsioni, I., Doulgeri, Z.: Slippage detection generalizing to grasping of unknown objects using machine learning with novel features. IEEE Robot. Autom. Lett. 3(2), 942–948 (2018)Google Scholar
  3. 3.
    Amirabdollahian, F., et al.: Accompany: acceptable robotics companions for ageing years multidimensional aspects of human-system interactions. In: The 6th International Conference on Human System Interaction, pp. 570–577. IEEE (2013)Google Scholar
  4. 4.
    Broadbent, E., Stafford, R., MacDonald, B.: Acceptance of healthcare robots for the older population: review and future directions. Int. J. Soc. Robot. 1(4), 319 (2009)CrossRefGoogle Scholar
  5. 5.
    Doumanoglou, A., Kouskouridas, R., Malassiotis, S., Kim, T.K.: Recovering 6D object pose and predicting next-best-view in the crowd. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3583–3592 (2016)Google Scholar
  6. 6.
    Endres, F., Hess, J., Engelhard, N., Sturm, J., Cremers, D., Burgard, W.: An evaluation of the RGB-D slam system. In: IEEE International Conference on Robotics and Automation, pp. 1691–1696. IEEE (2012)Google Scholar
  7. 7.
    Engelhardt, K.G.: An overview of health and human service robotics. Robot. Auton. Syst. 5(3), 205–226 (1989)CrossRefGoogle Scholar
  8. 8.
    Fischinger, D., et al.: Hobbit, a care robot supporting independent living at home: first prototype and lessons learned. Robot. Auton. Syst. 75, 60–78 (2016)CrossRefGoogle Scholar
  9. 9.
    Garcia, E., Jimenez, M.A., De Santos, P.G., Armada, M.: The evolution of robotics research. IEEE Robot. Autom. Mag. 14(1), 90–103 (2007)CrossRefGoogle Scholar
  10. 10.
    Sarantopoulos, I., Koveos, Y., Doulgeri, Z.: Grasping flat objects by exploiting non-convexity of the object and support surface. IEEE (2018, Accepted)Google Scholar
  11. 11.
    Jähne, C., Hirche, S.: Augmented invariance control for impedance-controlled robots with safety margins. IFAC PapersOnLine 50(1), 12053–12058 (2017)CrossRefGoogle Scholar
  12. 12.
    Korchut, A., et al.: Challenges for service robots requirements of elderly adults with cognitive impairments (2017)Google Scholar
  13. 13.
    Kostavelis, I., Giakoumis, D., Malassiotis, S., Tzovaras, D.: Human aware robot navigation in semantically annotated domestic environments. In: Antona, M., Stephanidis, C. (eds.) UAHCI 2016. LNCS, vol. 9738, pp. 414–423. Springer, Cham (2016). Scholar
  14. 14.
    Kostavelis, I., Giakoumis, D., Malassiotis, S., Tzovaras, D.: A POMDP design framework for decision making in assistive robots. In: Kurosu, M. (ed.) HCI 2017. LNCS, vol. 10271, pp. 467–479. Springer, Cham (2017). Scholar
  15. 15.
    Kostavelis, I., Kargakos, A., Giakoumis, D., Tzovaras, D.: Robot’s workspace enhancement with dynamic human presence for socially-aware navigation. In: Liu, M., Chen, H., Vincze, M. (eds.) ICVS 2017. LNCS, vol. 10528, pp. 279–288. Springer, Cham (2017). Scholar
  16. 16.
    Lawitzky, A., Althoff, D., Wollherr, D., Buss, M.: Dynamic window approach for omni-directional robots with polygonal shape. In: ICRA, pp. 2962–2963 (2011)Google Scholar
  17. 17.
    Leigh, A., Pineau, J.: Laser-based person tracking for clinical locomotion analysis. In: IROS Workshop on Rehabilitation and Assistive Robotics (2014)Google Scholar
  18. 18.
    Meuleau, N., Kim, K.E., Kaelbling, L.P., Cassandra, A.R.: Solving POMDPs by searching the space of finite policies. In: Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, pp. 417–426. Morgan Kaufmann Publishers Inc. (1999)Google Scholar
  19. 19.
    Spyridon, M.G., Eleftheria, M.: Classification of domestic robots. In: ARSA-Advanced Research in Scientific Areas, vol. 1, no. 7, p. 1693 (2012)Google Scholar
  20. 20.
    Stavropoulos, G., Giakoumis, D., Moustakas, K., Tzovaras, D.: Automatic action recognition for assistive robots to support MCI patients at home. In: 10th International Conference on PErvasive Technologies Related to Assistive Environments, pp. 366–371. ACM (2017)Google Scholar
  21. 21.
    Tinker, A., Lansley, P.: Introducing assistive technology into the existing homes of older people: feasibility, acceptability, costs and outcomes. J. Telemed. Telecare 11(1\(\_\)suppl), 1–3 (2005)CrossRefGoogle Scholar
  22. 22.
    Vasileiadis, M., Malassiotis, S., Giakoumis, D., Bouganis, C.S., Tzovaras, D.: Robust human pose tracking for realistic service robot applications. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1363–1372 (2017)Google Scholar
  23. 23.
    Yang, X., Tian, Y.: Effective 3D action recognition using eigenjoints. J. Vis. Commun. Image Represent. 25(1), 2–11 (2014)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Zielinska, T.: Professional and personal service robots. Int. J. Robot. Appl. Technol. 4(1), 63–82 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Ioannis Kostavelis
    • 1
    Email author
  • Dimitrios Giakoumis
    • 1
  • Georgia Peleka
    • 1
  • Andreas Kargakos
    • 1
  • Evangelos Skartados
    • 1
  • Manolis Vasileiadis
    • 1
    • 2
  • Dimitrios Tzovaras
    • 1
  1. 1.Centre for Research and Technology, Hellas Information Technologies Institute (CERTH/ITI)ThessalonikiGreece
  2. 2.Department of Electrical and Electronic EngineeringImperial College LondonLondonUK

Personalised recommendations