Learning to Recognise Objects and Situations to Control a Robot End-Effector
Heidemann G, Ritter H (2003)
KI, special issue on "Learning-Based Robot Vision" 2: 24-29.
Zeitschriftenaufsatz
| Veröffentlicht | Englisch
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Autor*in
Heidemann, Gunther;
Ritter, HelgeUniBi 
Einrichtung
Abstract / Bemerkung
View based representations have become very popular for recognition tasks. In this contribution, we argue that the potential of the approach is not yet fully tapped: Tasks need not to be “homogeneous”, i.e. there is no need to restrict a system e.g. to either “object classification ” or “gesture recognition”. Instead, qualitatively different problems like gesture recognition and scene evaluation can be handled simultaneously by the same system. This feature makes the view based approach a well suited tool for robotics as will be demonstrated for the domain of an end-effector camera. In the described scenario, the task is threefold: Recognition of object types, judging the stability of grasps on objects and hand gesture classification. As this task leads to a large variety of views, a neural network–based recognition architecture specifically designed to represent very non-linear distributions of samples representing views will be described. 1
Erscheinungsjahr
2003
Zeitschriftentitel
KI, special issue on "Learning-Based Robot Vision"
Band
2
Seite(n)
24-29
ISSN
0933-1875
Page URI
https://pub.uni-bielefeld.de/record/2714557
Zitieren
Heidemann G, Ritter H. Learning to Recognise Objects and Situations to Control a Robot End-Effector. KI, special issue on "Learning-Based Robot Vision". 2003;2:24-29.
Heidemann, G., & Ritter, H. (2003). Learning to Recognise Objects and Situations to Control a Robot End-Effector. KI, special issue on "Learning-Based Robot Vision", 2, 24-29.
Heidemann, Gunther, and Ritter, Helge. 2003. “Learning to Recognise Objects and Situations to Control a Robot End-Effector”. KI, special issue on "Learning-Based Robot Vision" 2: 24-29.
Heidemann, G., and Ritter, H. (2003). Learning to Recognise Objects and Situations to Control a Robot End-Effector. KI, special issue on "Learning-Based Robot Vision" 2, 24-29.
Heidemann, G., & Ritter, H., 2003. Learning to Recognise Objects and Situations to Control a Robot End-Effector. KI, special issue on "Learning-Based Robot Vision", 2, p 24-29.
G. Heidemann and H. Ritter, “Learning to Recognise Objects and Situations to Control a Robot End-Effector”, KI, special issue on "Learning-Based Robot Vision", vol. 2, 2003, pp. 24-29.
Heidemann, G., Ritter, H.: Learning to Recognise Objects and Situations to Control a Robot End-Effector. KI, special issue on "Learning-Based Robot Vision". 2, 24-29 (2003).
Heidemann, Gunther, and Ritter, Helge. “Learning to Recognise Objects and Situations to Control a Robot End-Effector”. KI, special issue on "Learning-Based Robot Vision" 2 (2003): 24-29.
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