Interactive online learning for obstacle classification on a mobile robot

We present an architecture for incremental online learning in high-dimensional feature spaces and apply it on a mobile robot. The model is based on learning vector quantization, approaching the stability-plasticity problem of incremental learning by adaptive insertions of representative vectors. We employ a cost-function-based learning vector quantization approach and introduce a new insertion strategy optimizing a cost-function based on a subset of samples. We demonstrate this model within a real-time application for a mobile robot scenario, where we perform interactive real-time learning of visual categories.

[1]  Heiko Wersing,et al.  Rapid Online Learning of Objects in a Biologically Motivated Recognition Architecture , 2005, DAGM-Symposium.

[2]  Barbara Caputo,et al.  Recognition with local features: the kernel recipe , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[3]  Maya Cakmak,et al.  Power to the People: The Role of Humans in Interactive Machine Learning , 2014, AI Mag..

[4]  K. N. Dollman,et al.  - 1 , 1743 .

[5]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .

[6]  Michael Biehl,et al.  Analysis of Flow Cytometry Data by Matrix Relevance Learning Vector Quantization , 2013, PloS one.

[7]  Teuvo Kohonen,et al.  Self-Organizing Maps, Third Edition , 2001, Springer Series in Information Sciences.

[8]  Shaoning Pang,et al.  An Incremental Principal Component Analysis for Chunk Data , 2006, 2006 IEEE International Conference on Fuzzy Systems.

[9]  Barbara Hammer,et al.  Computational Intelligence in Big Data , 2014 .

[10]  Heiko Wersing,et al.  Local Rejection Strategies for Learning Vector Quantization , 2014, ICANN.

[11]  Slobodan Vucetic,et al.  Learning Vector Quantization with adaptive prototype addition and removal , 2009, 2009 International Joint Conference on Neural Networks.

[12]  Michael Biehl,et al.  Adaptive Relevance Matrices in Learning Vector Quantization , 2009, Neural Computation.

[13]  Gert Cauwenberghs,et al.  Incremental and Decremental Support Vector Machine Learning , 2000, NIPS.

[14]  Sameer A. Nene,et al.  Columbia Object Image Library (COIL100) , 1996 .

[15]  Giulio Sandini,et al.  An incremental growing neural network and its application to robot control , 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium.

[16]  Klaus-Robert Müller,et al.  Incremental Support Vector Learning: Analysis, Implementation and Applications , 2006, J. Mach. Learn. Res..

[17]  Jodi Forlizzi,et al.  Service robots in the domestic environment: a study of the roomba vacuum in the home , 2006, HRI '06.

[18]  Barbara Caputo,et al.  Incremental learning for place recognition in dynamic environments , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Atsushi Sato,et al.  Generalized Learning Vector Quantization , 1995, NIPS.

[20]  Barbara Hammer,et al.  Computational Intelligence in Big Data [Guest Editorial] , 2014, IEEE Comput. Intell. Mag..

[21]  Yajie Tian,et al.  Handbook of face recognition , 2003 .

[22]  Mark W. Newman,et al.  Learning from a learning thermostat: lessons for intelligent systems for the home , 2013, UbiComp.

[23]  Sergio Bermejo,et al.  A new dynamic LVQ-based classifier and its application to handwritten character recognition , 1998, ESANN.

[24]  Heiko Wersing,et al.  Rejection strategies for learning vector quantization , 2014, ESANN.

[25]  José G. Dias,et al.  The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data , 2008, IBERAMIA.

[26]  David Filliat.  Interactive learning of visual topological navigation , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[27]  Anil K. Jain,et al.  Handbook of Face Recognition, 2nd Edition , 2011 .

[28]  Gert Cauwenberghs,et al.  SVM incremental learning, adaptation and optimization , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[29]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[30]  Steven C. H. Hoi,et al.  LIBOL: a library for online learning algorithms , 2014, J. Mach. Learn. Res..

[31]  Robi Polikar,et al.  Guest Editorial Learning in Nonstationary and Evolving Environments , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[32]  Heiko Wersing,et al.  Learning Optimized Features for Hierarchical Models of Invariant Object Recognition , 2003, Neural Computation.