Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot

Reinhart F, Steil JJ (2008)
In: Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award. Stoica A, Tunsel E, Huntsberger T, Arslan T, Vijayakumar S, El-Rayis AO, European Center for Secure Information and Systems, The University of Edinburgh (Eds); , 1. Edinburgh: IEEE: 35-40.

Konferenzbeitrag | Veröffentlicht | Englisch
 
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Herausgeber*in
Stoica, Adrian; Tunsel, Eddie; Huntsberger, Terry; Arslan, Tughrul; Vijayakumar, Sethu; El-Rayis, Ahmed O.
herausgebende Körperschaft
European Center for Secure Information and Systems; The University of Edinburgh
Stichworte
CoR-Lab Publication
Erscheinungsjahr
2008
Titel des Konferenzbandes
Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award
Band
1
Seite(n)
35-40
ISBN
978-0-7695-3272-1
Page URI
https://pub.uni-bielefeld.de/record/2142077

Zitieren

Reinhart F, Steil JJ. Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot. In: Stoica A, Tunsel E, Huntsberger T, et al., eds. Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award. Vol 1. Edinburgh: IEEE; 2008: 35-40.
Reinhart, F., & Steil, J. J. (2008). Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot. In A. Stoica, E. Tunsel, T. Huntsberger, T. Arslan, S. Vijayakumar, A. O. El-Rayis, European Center for Secure Information and Systems, et al. (Eds.), Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award (Vol. 1, pp. 35-40). Edinburgh: IEEE.
Reinhart, Felix, and Steil, Jochen J. 2008. “Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot”. In Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award, ed. Adrian Stoica, Eddie Tunsel, Terry Huntsberger, Tughrul Arslan, Sethu Vijayakumar, Ahmed O. El-Rayis, European Center for Secure Information and Systems, and The University of Edinburgh, 1:35-40. Edinburgh: IEEE.
Reinhart, F., and Steil, J. J. (2008). “Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot” in Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award, Stoica, A., Tunsel, E., Huntsberger, T., Arslan, T., Vijayakumar, S., El-Rayis, A. O., European Center for Secure Information and Systems, and The University of Edinburgh eds., vol. 1, (Edinburgh: IEEE), 35-40.
Reinhart, F., & Steil, J.J., 2008. Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot. In A. Stoica, et al., eds. Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award. no.1 Edinburgh: IEEE, pp. 35-40.
F. Reinhart and J.J. Steil, “Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot”, Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award, A. Stoica, et al., eds., vol. 1, Edinburgh: IEEE, 2008, pp.35-40.
Reinhart, F., Steil, J.J.: Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot. In: Stoica, A., Tunsel, E., Huntsberger, T., Arslan, T., Vijayakumar, S., El-Rayis, A.O., European Center for Secure Information and Systems, and The University of Edinburgh (eds.) Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award. 1, p. 35-40. IEEE, Edinburgh (2008).
Reinhart, Felix, and Steil, Jochen J. “Recurrent neural associative learning of forward and inverse kinematics for movement generation of the redundant PA-10 robot”. Int. Symp. Learning Adaptive Behavior in Robotic Systems, best paper award. Ed. Adrian Stoica, Eddie Tunsel, Terry Huntsberger, Tughrul Arslan, Sethu Vijayakumar, Ahmed O. El-Rayis, European Center for Secure Information and Systems, and The University of Edinburgh. Edinburgh: IEEE, 2008.Vol. 1. 35-40.
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