Active Intention Inference for Robot-Human Collaboration

Active Intention Inference for Robot-Human Collaboration

Hsien-I Lin Xuan-Anh Nguyen Wei-Kai Chen

Graduate Institute of Automation Technology National Taipei University of Technology, Taipei, Taiwan

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Understanding human intention is an important ability for an intelligent robot to collaborate with a human to accomplish various tasks. During collaboration, a robot with such ability can predict the successive actions that a human partner intends to perform, provide necessary assistance and support, and remind for the missing and failure actions from the human to achieve a desired task purpose. This paper presents a framework that allows a robot to automatically recognize and infer the action intention of a human partner based on visualization, in which an inverse-reinforcement learning (IRL) system is learnt based on the observed human demonstration and used to infer the human successive actions. Compared to other systems based on reinforcement learning, the reward of a Markov-Decision process (MDP) is directly learned from the demonstration. In our experiment, we provide some examples of the proposed framework which yields promising results with coffee-making and pick-and-place tasks. Regarding to the human-intention model based on IRL, the coffee-making experiment indicates that the action is globally predicted because the action of putting down the water pot is selected instead of pouring water when the cup is already filled with water.


human gesture recognition; human-robot collaboration; Markov decision process


[1] Bascetta, L., Ferretti, G., Rocco, P. & Ardo, H., Towards safe human-robot interaction in robotic cells: An approach based on visual tracking and intention estimation. IEEE/ RSJ IROS, San Francisco, pp. 2971–2978, 2011.

[2] Kanno, T., Nakata, K. & Furuta, K., A method for team intention inference. International Journal of Human-Computer Studies, 58(4), 393–413, 2003.

[3] Zhou, S. & Wu, C. H., A recognition method for drivers intention based on genetic algorithm and ant colony optimization. Conference on Natural Computation, Shanghai, pp. 1033–1037, 2011.

[4] Wu, P., Wang, Z. & Chen, J. H., Research on attack intention recognition based on graphical model. Proceedings Of International Conference on Information Assurance and Security, Shanghai, 1, pp. 360–363, 2009.

[5] Jin, L., Hou, H. & Jiang, Y., Driver intention recognition based on continuous hidden Markov model. Conference on Transportation, Mechanical, and Electrical Engineering (TMEE), 1, pp. 739–742, 2011.

[6] Gehrig, D., Khne, H., Wrner, A. & Schultz, T., HMM-based human motion recognition with optical flow data. Proceedings of the 9th IEEE-RAS International Conference. on Humanoid Robots, Paris, pp. 425–430, 2009.

[7] 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.

[8] Jeon, H., Kim, T. & Choi, J., Ontology-based user intention recognition for proactive planning of intelligent robot behavior. International Conference On Multimedia and Ubiquitous Engineering, Busan, pp. 244–248, 2008.

[9] Huang, Y.C., Young, H.P., Ko, C. H. & Young, K.Y., Design and implementation of a robot control system with traded and shared control capability. Proceeding Of Asian Control Conference, pp. 311–316, 2011.

[10] McGhan, C.L.R., Nasir, A. & Atkins, E.M., Human intention prediction using Markov Decision Processes. Journal of Aerospace Information Systems, 12(5), pp. 393–397, 2015.

[11] Song, D., Kyriazis, N., Oikonomidis, I., Papazov, C., Argyros, A., Burschka, A. & Kragic, D., Predicting human intention in visual observations of hand/object interac- tions. IEEE International Conference Robotics and Automation, pp. 1608–1615, 2013.

[12] Keskinpala, H.K., Adams, J.A. & Kawamura, K., PDA-based human robotic interface. IEEE International Conference on SMC, Washington, DC, pp. 1310–1315, 2003.

[13] Breazeal, C., Emotion and social humanoid robots. International Journal of Human- Computer Studies, 59, pp. 119–155, 2003.

[14] Ng, A. Y. & Russell, S., Algorithms for inverse reinforcement learning. Proceedings of ICML, pp. 663–670, 2000.

[15] Nagi, J., Ducatelle, F., Caro, G., Ciresan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J. & Gambardella, L.M., Max-pooling convolutional neural networks for vision-based hand gesture recognition. IEEE International Conference on signal and Image Processing Applications, pp. 342–347, 2011.

[16] Lin, H.I. & Chiang, Y.P., Understanding human hand gestures for learning robot pick- and-place tasks. International Journal of Advanced Robotic Systems, 12(5), p. 49, 2015.

[17] Lecun, Y., Bottou, L., Bengio, Y. & Haffner, P., Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86, pp. 2278–2324, 1998.

[18] Lin, H.I. & Chen, W-K., Human intention recognition using markov decision processes. Proceedings of International Conference on Automatic Control (CACS), pp. 240–343, 2014.