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Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints
Kun Li, Max Q.-H. Meng
Engineering ›› 2015, Vol. 1 ›› Issue (1) : 79-84.
PDF(1714 KB)
PDF(1714 KB)
Personalizing a Service Robot by Learning Human Habits from Behavioral Footprints
For a domestic personal robot, personalized services are as important as predesigned tasks, because the robot needs to adjust the home state based on the operator’s habits. An operator’s habits are composed of cues, behaviors, and rewards. This article introduces behavioral footprints to describe the operator’s behaviors in a house, and applies the inverse reinforcement learning technique to extract the operator’s habits, represented by a reward function. We implemented the proposed approach with a mobile robot on indoor temperature adjustment, and compared this approach with a baseline method that recorded all the cues and behaviors of the operator. The result shows that the proposed approach allows the robot to reveal the operator’s habits accurately and adjust the environment state accordingly.
personalized robot / habit learning / behavioral footprints
| [1] |
W. Wood, D. T. Neal. A new look at habits and the habit-goal interface. Psychol. Rev., 2007, 114(4): 843–863
|
| [2] |
W. Meeussen,
|
| [3] |
R. B. Rusu, I. A. Sucan, B. P. Gerkey, S. Chitta, M. Beetz, L. E. Kavraki. Real-time perception-guided motion planning for a personal robot. In: Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, 2009: 4245–4252
|
| [4] |
J. F. Gorostiza,
|
| [5] |
K. A. Wyrobek, E. H. Berger, H. F. M. Van der Loos, J. K. Salisbury. Towards a personal robotics development platform: Rationale and design of an intrinsically safe personal robot. In: Proceedings of IEEE International Conference on Robotics and Automation, 2008: 2165–2170
|
| [6] |
E. Falcone, R. Gockley, E. Porter, I. Nourbakhsh. The personal rover project: The comprehensive design of a domestic personal robot. Robot. Auton. Syst., 2003, 42(3¯4): 245–258
|
| [7] |
L. Tonin, T. Carlson, R. Leeb, J. del R. Millán. Brain-controlled telepresence robot by motor-disabled people. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2011: 4227–4230
|
| [8] |
T. C. Tsai, Y. L. Hsu, A. I. Ma, T. King, C. H. Wu. Developing a telepresence robot for interpersonal communication with the elderly in a home environment. Telemed. J. E Health, 2007, 13(4): 407–424
|
| [9] |
P. R. Liu, M. Q. H. Meng, P. X. Liu, F. F. L. Tong, X. J. Chen. A telemedicine system for remote health and activity monitoring for the elderly. Telemed. J. E Health, 2006, 12(6): 622–631
|
| [10] |
M. Baeg, J. H. Park, J. Koh, K. W. Park, M. H. Baeg. Building a smart home environment for service robots based on RFID and sensor networks. In: Proceedings of International Conference on Control, Automation and Systems, 2007: 1078–1082
|
| [11] |
N. Oliver, A. Garg, E. Horvitz. Layered representations for learning and inferring office activity from multiple sensory channels. Comput. Vis. Image Underst., 2004, 96(2): 163–180
|
| [12] |
S. Fine, Y. Singer, N. Tishby. The hierarchical hidden Markov model: Analysis and applications. Mach. Learn., 1998, 32(1): 41–62
|
| [13] |
R. S. Sutton, A. G. Barto. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press, 1998
|
| [14] |
B. D. Argall, S. Chernova, M. Veloso, B. Browning. A survey of robot learning from demonstration. Robot. Auton. Syst., 2009, 57(5): 469–483
|
| [15] |
P. Abbeel, A. Y. Ng. Apprenticeship learning via inverse reinforcement learning. In: Proceedings of the Twenty-first International Conference on Machine Learning, 2004: 1
|
| [16] |
A. Y. Ng, S. J. Russell. Algorithms for inverse reinforcement learning. In: Proceedings of the Seventeenth International Conference on Machine Learning, 2000: 663–670
|
| [17] |
G. Grisetti, C. Stachniss, W. Burgard. Improved techniques for grid mapping with Rao-Blackwellized particle filters. IEEE Trans. Robot., 2007, 23(1): 34–46
|
| [18] |
T. Joachims. Making large-scale SVM learning practical. In: B. Schölkopf, C. J. C. Burges, A. J. Smola, eds. Advances in Kernel Methods: Support Vector Learning. Cambridge, MA: MIT Press, 1999: 169–184
|
/
| 〈 |
|
〉 |
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