Abstract
In order for assistive robots to collaborate effectively with humans, they must be endowed with the ability to perceive scenes and more importantly, recognize human intentions. These intentions are often inferred from observed physical actions and direct communication from fully-functional individuals. For individuals with reduced capabilities, it may be difficult or impossible to perform physical actions or easily communicate. Therefore, their intentions must be inferred differently. To this end, we propose an intention recognition framework that is appropriate for persons with limited physical capabilities. This framework determines and learns human intentions based on scene objects, the actions that can be performed on them, and past interaction history. It is based on a Markov model formulation entitled Object-Action Intention Networks, which constitute the crux of a computer vision-based human-robot collaborative system that reduces the necessary interactions for communicating tasks to a robot. Evaluations were conducted on multiple scenes comprised of multiple possible object categories and actions. We achieve approximately 81% reduction in interactions overall after learning, when compared to other intention recognition approaches.
Chapter PDF
Similar content being viewed by others
References
Carlson, T., Demiris, Y.: Collaborative Control for a Robotic Wheelchair: Evaluation of Performance, Attention, and Workload. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 42(3), 876–888 (2012)
Charniak, E., Goldman, R.P.: A Bayesian model of plan recognition. Artificial Intelligence 64, 53–79 (1993)
Collet, A., Martinez, M., Srinivasa, S.S.: The MOPED framework: Object recognition and pose estimation for manipulation. The International Journal of Robotics Research 30(10), 1284–1306 (2011)
Collet, A., Berenson, D., Srinivasa, S.S., Ferguson, D.: Object recognition and full pose registration from a single image for robotic manipulation. In: 2009 IEEE International Conference on Robotics and Automation, pp. 48–55, May 2009
Dalal, N., Triggs, B.: Histograms of Oriented Gradients for Human Detection. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 886–893. IEEE (2005)
Demeester, E., Huntermann, A., Vanhooydonck, D., Vanacker, G., Brussel, H.V., Nuttin, M.: User-adapted plan recognition and user-adapted shared control: A Bayesian approach to semi-autonomous wheelchair driving. Autonomous Robots 24, 193–211 (2007)
Duncan, K., Sarkar, S., Alqasemi, R., Dubey, R.: Multi-scale Superquadric Fitting for Efficient Shape and Pose Recovery of Unknown Objects. In: International Conference on Robotics and Automation (2013)
Heinze, C.: Modelling intention recognition for intelligent agent systems. Ph.D. thesis, The University of Melbourne, Melbourne, Australia (2003)
Kelley, R., Tavakkoli, A., King, C., Ambardekar, A., Nicolescu, M., Nicolescu, M.: Context-Based Bayesian Intent Recognition. IEEE Transactions on Autonomous Mental Development 4(3), 215–225 (2012)
Kelley, R., Wigand, L., Hamilton, B., Browne, K., Nicolescu, M., Nicolescu, M.: Deep networks for predicting human intent with respect to objects. In: Proceedings of the Seventh Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2012, p. 171 (2012)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques, 1 edn. MIT Press (2010)
Lowe, D.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60(2), 91–110 (2004)
Madry, M., Song, D., Ek, C.H., Kragic, D.: Robot bring me something to drink from: Object Representation For Transferring Task Specific Grasps. In: IEEE International Conference on Robotics and Automation (2012)
Madry, M., Song, D., Kragic, D.: From Object Categories to Grasp Transfer Using Probabilistic Reasoning. In: IEEE International Conference on Robotics and Automation (2012)
Meger, D., Forssén, P.E., Lai, K., Helmer, S., McCann, S., Southey, T., Baumann, M., Little, J.K., Lowe, D.G.: Curious George: An attentive semantic robot. Robotics and Autonomous Systems 56, 503–511 (2008)
RamÃk, D.M., Madani, K., Sabourin, C.: From visual patterns to semantic description: A cognitive approach using artificial curiosity as the foundation. Pattern Recognition Letters 34, 1577–1588 (2013)
Rusu, R., Blodow, N., Beetz, M.: Fast Point Feature Histograms (FPFH) for 3D Registration. In: IEEE International Conference on Robotics and Automation, pp. 3212–3217. IEEE (2009)
Rusu, R., Blodow, N., Marton, Z.: Close-range scene segmentation and reconstruction of 3D point cloud maps for mobile manipulation in domestic environments. In: International Conference on Intelligent Robots and Systems, pp. 3–8 (2009)
Tahboub, K.A.: Intelligent Human-Machine Interaction Based on Dynamic Bayesian Networks Probabilistic Intention Recognition. Journal of Intelligent and Robotic Systems 45(1), 31–52 (2006)
Tavakkoli, A., Kelley, R., King, C., Nicolescu, M., Nicolescu, M., Bebis, G.: A Vision-Based Architecture for Intent Recognition. In: Bebis, G., Boyle, R., Parvin, B., Koracin, D., Paragios, N., Tanveer, S.-M., Ju, T., Liu, Z., Coquillart, S., Cruz-Neira, C., Müller, T., Malzbender, T. (eds.) ISVC 2007, Part II. LNCS, vol. 4842, pp. 173–182. Springer, Heidelberg (2007)
Vanhooydonch, D., Demeester, E., Nuttin, M., Brussel, H.V.: Shared Control for Intelligent Wheelchairs: An Implicit Estimation of the User Intention. In: 2003 International Workshop on Advances in Service Robotics, pp. 176–182 (2003)
Zhu, C., Sun, W., Sheng, W.: Wearable Sensors based Human Intention Recognition in Smart Assisted Living Systems. In: International Conference on Information and Automation, pp. 954–959 (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Duncan, K., Sarkar, S., Alqasemi, R., Dubey, R. (2015). Scene-Dependent Intention Recognition for Task Communication with Reduced Human-Robot Interaction. In: Agapito, L., Bronstein, M., Rother, C. (eds) Computer Vision - ECCV 2014 Workshops. ECCV 2014. Lecture Notes in Computer Science(), vol 8927. Springer, Cham. https://doi.org/10.1007/978-3-319-16199-0_51
Download citation
DOI: https://doi.org/10.1007/978-3-319-16199-0_51
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16198-3
Online ISBN: 978-3-319-16199-0
eBook Packages: Computer ScienceComputer Science (R0)