Abstract
Gesture-based interfaces offer the possibility of an intuitive command language for assistive robotics and ubiquitous computing. As an individual’s health changes with age, their ability to consistently perform standard gestures may decrease, particularly towards the end of life. Thus, such interfaces will need to be capable of learning commands which are not choreographed ahead of time by the system designers. This circumstance illustrates the need for a system which engages in lifelong learning and is capable of discerning new gestures and the user’s desired response to them. This paper describes an innovative approach to lifelong learning based on clustered gesture representations identified through the Growing Neural Gas algorithm. The simulated approach utilizes a user-generated reward signal to progressively refine the response of an assistive robot toward a preferred goal configuration.
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Hamker, F.H.: Life-long learning Cell Structures-continuously learning without catastrophic interference. Neural Networks 14(4), 551–573 (2001)
Yanik, P.M., Merino, J., Threatt, A.L., Manganelli, J., Brooks, J.O., Green, K.E., Walker, I.D.: A Gesture Learning Interface for Simulated Robot Path Shaping with a Human Teacher. IEEE Transactions on Human-Machine Systems 44(1), 41–54 (2014)
Fritzke, B.: A Growing Neural Gas Network Learns Topologies. Advances in Neural Information Processing Systems 7(7), 625–632 (1995)
Grossberg, S.: Nonlinear neural networks: Principles, mechanisms, and architectures. Neural Networks 1(1), 17–61 (1988)
Kohonen, T.: The self-organizing map. Proc. of the IEEE 78(9), 1464–1480 (1990)
Holmström, J.: Growing Neural Gas: Experiments with GNG, GNG with Utility and Supervised GNG. Master’s thesis, Uppsala University – Department of Information Technology (2002)
Fritzke, B.: A self-organizing network that can follow non-stationary distributions. In: Gerstner, W., Hasler, M., Germond, A., Nicoud, J.-D. (eds.) ICANN 1997. LNCS, vol. 1327, pp. 613–618. Springer, Heidelberg (1997)
Furao, S., Hasegawa, O.: An incremental network for on-line unsupervised classification and topology learning. Neural Networks 19(1), 90–106 (2006)
ASL Pro Website, http://www.aslpro.com/cgi-bin/aslpro/aslpro.cgi
Rao, C., Yilmaz, A., Shah, M.: View-Invariant Representation and Recognition of Actions. International Journal of Computer Vision 50(2), 203–226 (2002)
Microsoft Xbox 360 + Kinect Website, http://www.xbox.com/en-US/kinect
Kaplan, F., Oudeyer, P.Y., Kubinyi, E., Miklósi, A.: Robotic clicker training. Robotics and Autonomous Systems 38(3), 197–206 (2002)
Yanik, P.M.: Gesture-Based Robot Path Shaping. PhD thesis, Clemson University (2013)
Touzet, C.F.: Neural reinforcement learning for behaviour synthesis. Robotics and Autonomous Systems 22(3), 251–281 (1997)
Estrada, E.: The Structure of Complex Networks: Theory and Applications. Oxford (2012)
Tucker, A.: Applied Combinatorics, 6th edn. Wiley (2007)
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Yanik, P.M. et al. (2014). A Method for Lifelong Gesture Learning Based on Growing Neural Gas. In: Kurosu, M. (eds) Human-Computer Interaction. Advanced Interaction Modalities and Techniques. HCI 2014. Lecture Notes in Computer Science, vol 8511. Springer, Cham. https://doi.org/10.1007/978-3-319-07230-2_19
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DOI: https://doi.org/10.1007/978-3-319-07230-2_19
Publisher Name: Springer, Cham
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