Skip to main content
SpringerLink
Log in

Predicting the Intention of Human Activities for Real-Time Human-Robot Interaction (HRI)

  • Conference paper
  • First Online:
Social Robotics (ICSR 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9979))

Included in the following conference series:

Abstract

The modeling methodology of Human-object relation is needed for human intention recognition in assistive robotics. When helping the elderly, the future action prediction is an essential task in real-time human-robot interaction. Since the future actions of humans are ambiguous, robots need to carefully conclude about the appropriate action. This requires a mathematical model to evaluate all possible future actions, corresponding probabilities and the possible relation between humans and the objects.

Our contribution is the modeling methodology for the human activities using the probabilistic state machine (PSM). Not only the objects, but also latent human poses and the relationships between humans and the objects are here considered. The probabilistic model allows uncertainties and variations in the object affordances. In experiments, we show how the intention recognition w.r.t. drinking activity is analysed using our approach.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bandyopadhyay, T., Won, K.S., Frazzoli, E., Hsu, D., Lee, W.S., Rus, D.: Intention-aware motion planning. In: Frazzoli, E., Lozano-Perez, T., Roy, N., Rus, D. (eds.) Algorithmic Foundations of Robotics X. STAR, vol. 86, pp. 475–491. Springer, Heidelberg (2013). doi:10.1007/978-3-642-36279-8_29

    Chapter  Google Scholar 

  2. Faraway, J.J., Reed, M.P., Wang, J.: Modelling three-dimensional trajectories by using bézier curves with application to hand motion. J. Royal Stat. Soc.: Seri. C (Appl. Statist.) 56(5), 571–585 (2007)

    Article  Google Scholar 

  3. Gopnik, A., Slaughter, V., Meltzoff, A.: Changing your views: How understanding visual perception can lead to a new theory of the mind. Children’s early understanding of mind: Origins and development, pp. 157–181 (1994)

    Google Scholar 

  4. Jansen, B., Belpaeme, T.: A computational model of intention reading in imitation. Robot. Autonom. Syst. 54(5), 394–402 (2006)

    Article  Google Scholar 

  5. Jiang, Y., Koppula, H., Saxena, A.: Modeling 3d environments through hidden human context (2015)

    Google Scholar 

  6. Koppula, H.S., Gupta, R., Saxena, A.: Learning human activities and object affordances from rgb-d videos. Int. J. Robot. Res. 32(8), 951–970 (2013)

    Article  Google Scholar 

  7. Koppula, H.S., Saxena, A.: Physically grounded spatio-temporal object affordances. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 831–847. Springer, Heidelberg (2014). doi:10.1007/978-3-319-10578-9_54

    Google Scholar 

  8. Microsoft. Kinect (2015). https://developer.microsoft.com/en-us/windows/kinect. Accessed 11-Feb-2015

  9. Ramirez-Amaro, K., Beetz, M., Cheng, G.: Automatic segmentation and recognition of human activities from observation based on semantic reasoning. In: 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014), pp. 5043–5048. IEEE (2014)

    Google Scholar 

  10. Sense, P.: Openni skeleton tracking (2015). http://openni.org. Accessed 19 Nov 2015

  11. O. soure robotics foundation. RVIZ (2015). http://wiki.ros.org/rviz. Accessed 19 Nov 2015

  12. Stulp, F., Grizou, J., Busch, B., Lopes, M.: Facilitating intention prediction for humans by optimizing robot motions. In: 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 1249–1255. IEEE (2015)

    Google Scholar 

  13. Yuan, F., Prinet, V., Yuan, J.: Middle-level representation for human activities recognition: the role of spatio-temporal relationships. In: Kutulakos, K.N. (ed.) ECCV 2010. LNCS, vol. 6553, pp. 168–180. Springer, Heidelberg (2012). doi:10.1007/978-3-642-35749-7_13

    Chapter  Google Scholar 

Download references

Acknowledgments

Authors of this work were supported by the Dean’s grant (Grant No 504/02673/1132/42.000100) funded by Warsaw University of Technology. Moreover, it is also supported by the HERITAGE project (Erasmus Mundus Action 2 Strand 1 Lot 11, EAECA/42/11) funded by the European Commission.

Author information

Authors and Affiliations

  1. Faculty of Power and Aeronautical Engineering (MEiL), Warsaw University of Technology, ul. Nowowiejska 24, 00-665, Warsaw, Poland

    Vibekananda Dutta & Teresa Zielinska

Authors
  1. Vibekananda Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Teresa Zielinska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Vibekananda Dutta .

Editor information

Editors and Affiliations

  1. Department of Electrical Engineering, The University of Kansas, Lawrence, Indiana, USA

    Arvin Agah

  2. Department of Mechanical Engineering, Qatar University, Doha, Qatar

    John-John Cabibihan

  3. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, USA

    Ayanna M. Howard

  4. Department of Systems Engineering and Automation, University Carlos III de Madrid, madrid, Spain

    Miguel A. Salichs

  5. Department of Mechanical, Aerospace and Biomedical Engineering, University of Tennessee, Knoxville, Tennessee, USA

    Hongsheng He

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

Dutta, V., Zielinska, T. (2016). Predicting the Intention of Human Activities for Real-Time Human-Robot Interaction (HRI). In: Agah, A., Cabibihan, JJ., Howard, A., Salichs, M., He, H. (eds) Social Robotics. ICSR 2016. Lecture Notes in Computer Science(), vol 9979. Springer, Cham. https://doi.org/10.1007/978-3-319-47437-3_71

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-47437-3_71

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-47436-6

  • Online ISBN: 978-3-319-47437-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation