Active Perception and Exploratory Robotics

Most past and present work in machine perception has involved extensive static analysis of passively sampled data. However, it should be axiomatic that perception is not passive, but active. Furthermore, most past and current robotics research use rather rigid assumptions, models about the world, objects and their relationships. It is not so difficult to see that these assumptions, most of the time, in realistic situations do not hold, and hence, the robots do not perform to the designer’s expectations.

[1]  J. Hartmanis,et al.  Algebraic Structure Theory Of Sequential Machines , 1966 .

[2]  Jay Martin Tenenbaum,et al.  Accommodation in computer vision , 1971 .

[3]  N.R. Malik,et al.  Graph theory with applications to engineering and computer science , 1975, Proceedings of the IEEE.

[4]  Franc Solina,et al.  Errors in stereo due to quantization , 1985 .

[5]  Steven W. Zucker,et al.  Early orientation selection: Tangent fields and the dimensionality of their support , 1985, Comput. Vis. Graph. Image Process..

[6]  Thomas O. Binford,et al.  On Detecting Edges , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Steven W. Zucker,et al.  The Local Structure of Image Discontinuities in One Dimension , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  R. Bajcsy,et al.  Shape recovery and segmentation with deformable part models , 1987 .

[9]  Peter K. Allen Robotic Object Recognition Using Vision and Touch , 1987 .

[10]  R. Klatzky,et al.  Hand movements: A window into haptic object recognition , 1987, Cognitive Psychology.

[11]  Theodosios Pavlidis,et al.  Integrating region growing and edge detection , 1988, Proceedings CVPR '88: The Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Sharon A. Stansfield,et al.  A Robotic Perceptual System Utilizing Passive Vision and Active Touch , 1988, Int. J. Robotics Res..

[13]  Yiannis Aloimonos,et al.  Active vision , 2004, International Journal of Computer Vision.

[14]  Ruzena Bajcsy,et al.  Research on Symbolic Inference in Computational Vision , 1989 .