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Volumn , Issue , 2014, Pages 1110-1115

Learning visual forward models to compensate for self-induced image motion

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL ROBOTS; FORECASTING; RADIAL BASIS FUNCTION NETWORKS; COMPLEX NETWORKS; MOTION ANALYSIS; ROBOTS;

EID: 84937605949     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROMAN.2014.6926400     Document Type: Conference Paper
Times cited : (4)

References (20)
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  • 2
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    • A discrete computational model of sensorimotor contingencies for object perception and control of behavior
    • Shanhai, China
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    • Maye, A.1    Engel, A.K.2
  • 10
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    • (2009) Applied Bionics and Biomechanics , vol.6 , Issue.3-4 , pp. 345-354
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  • 11
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    • G. Kootstra, N. Wilming, N. M. Schmidt, M. Djurfeldt, D. Kragic, and P. Konig, "Learning and adaptation of sensorimotor contingencies: Prism-Adaptation, a case study, " in From Animals to Animats 12. Springer, 2012, pp. 341-350.
    • (2012) From Animals to Animats , vol.12 , pp. 341-350
    • Kootstra, G.1    Wilming, N.2    Schmidt, N.M.3    Djurfeldt, M.4    Kragic, D.5    Konig, P.6
  • 13
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    • Perception through visuomotor anticipation in a mobile robot
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  • 16
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    • Training and application of a visual forward model for a robot camera head
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.