Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control

Reinhart R, Shareef Z, Steil JJ (2017)
Sensors 17(2): 311.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Download
Es wurden keine Dateien hochgeladen. Nur Publikationsnachweis!
Autor*in
Reinhart, René; Shareef, Zeeshan; Steil, Jochen J.UniBi
Erscheinungsjahr
2017
Zeitschriftentitel
Sensors
Band
17
Ausgabe
2
Art.-Nr.
311
ISSN
1424-8220
Page URI
https://pub.uni-bielefeld.de/record/2909148

Zitieren

Reinhart R, Shareef Z, Steil JJ. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control. Sensors. 2017;17(2): 311.
Reinhart, R., Shareef, Z., & Steil, J. J. (2017). Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control. Sensors, 17(2), 311. https://doi.org/10.3390/s17020311
Reinhart, René, Shareef, Zeeshan, and Steil, Jochen J. 2017. “Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control”. Sensors 17 (2): 311.
Reinhart, R., Shareef, Z., and Steil, J. J. (2017). Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control. Sensors 17:311.
Reinhart, R., Shareef, Z., & Steil, J.J., 2017. Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control. Sensors, 17(2): 311.
R. Reinhart, Z. Shareef, and J.J. Steil, “Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control”, Sensors, vol. 17, 2017, : 311.
Reinhart, R., Shareef, Z., Steil, J.J.: Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control. Sensors. 17, : 311 (2017).
Reinhart, René, Shareef, Zeeshan, and Steil, Jochen J. “Hybrid Analytical and Data-Driven Modeling for Feed-Forward Robot Control”. Sensors 17.2 (2017): 311.

1 Zitation in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

Control Strategies for Soft Robotic Manipulators: A Survey.
George Thuruthel T, Ansari Y, Falotico E, Laschi C., Soft Robot 5(2), 2018
PMID: 29297756

35 References

Daten bereitgestellt von Europe PubMed Central.

Inertial parameters identification and joint torques estimation with proximal force/torque sensing
Traversaro S., Prete A.D., Ivaldi S., Nori F.., 0
Optimal Excitation and Identification of the Dynamic Model of Robotic Systems with Compliant Actuators
Vantilt J., Aertbeliën E., Groote F.D., Schutter J.D.., 0
Learning feedforward control of a flexible beam
Velthuis W., de T., van J.., 0
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Schaal S., Atkeson C.G., Vijayakumar S.., 2002
Computed torque control with nonparametric regression models
Nguyen-Tuong D., Seeger M., Peters J.., 0
Goal Babbling Permits Direct Learning of Inverse Kinematics
Rolf M., Steil J., Gienger M.., 2010
Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk
Rolf M., Steil J.., 2014
Bayesian robot system identification with input and output noise.
Ting JA, D'Souza A, Schaal S., Neural Netw 24(1), 2010
PMID: 20863655
Learning Inverse Dynamics: A Comparison
Nguyen-Tuong D., Peters J., Seeger M., Schölkopf B.., 0
Using model knowledge for learning inverse dynamics
Nguyen-Tuong D., Peters J.., 0
Real-time deep learning of robotic manipulator Inverse Dynamics
Polydoros A.S., Nalpantidis L., Krüger V.., 0
Design and Implementation of Intelligent Control Software for a Dough Kneader
Oestersoetebier F., Traphoener P., Reinhart R.F., Wessels S., Traechtler A.., 2016
Independent Joint Learning: A novel task-to-task transfer learning scheme for robot models
Um T., Park M.S., Park J.M.., 0
Independent Joint Learning in Practice: Local Error Estimates to Improve Inverse Dynamics Control
Caluwaerts K., Steil J.J.., 0
Hybrid Mechanical and Data-driven Modeling Improves Inverse Kinematic Control of a Soft Robot
Reinhart R.F., Steil J.J.., 2016
Improving the Inverse Dynamics Model of the KUKA LWR IV+ Using Independent Joint Learning
Shareef Z., Mohammadi P., Steil J.., 2016
Generalizing a learned inverse dynamic model of KUKA LWR IV+ for load variations using regression in the model space
Shareef Z., Reinhart F., Steil J.., 0
Towards robust online inverse dynamics learning
Meier F., Kappler D., Ratliff N., Schaal S.., 0
Constant curvature continuum kinematics as fast approximate model for the Bionic Handling Assistant
Rolf M., Steil J.., 0
A Variable Curvature Continuum Kinematics for Kinematic Control of the Bionic Handling Assistant
Mahl T., Hildebrandt A., Sawodny O.., 2014
Walking control of fully actuated robots based on the bipedal slip model
Garofalo G., Ott C., Albu-Schäffer A.., 0
Many regression algorithms, one unified model: A review.
Stulp F, Sigaud O., Neural Netw 69(), 2015
PMID: 26087306
Reliable Integration of Continuous Constraints into Extreme Learning Machines
Neumann K., Rolf M., Steil J.J.., 2013
The Bionic Handling Assistant: A success story of additive manufacturing
Grzesiak A., Becker R., Verl A.., 2011
Null space redundancy learning for a flexible surgical robot
Bruno D., Calinon S., Caldwell D.G.., 0
A multi-level control architecture for the bionic handling assistant
Rolf M., Neumann K., Queißer J., Reinhart R., Nordmann A., Steil J.., 2015

Spong M., Vidyasagar M.., 1989
VICON Motion Tracking Systems
AUTHOR UNKNOWN, 0
Extreme learning machine: A new learning scheme of feedforward neural networks
Huang G.B., Zhu Q.Y., Siew C.K.., 0
Feedforward neural networks with random weights
Schmidt W., Kraaijveld M., Duin R.., 0
Multivariable Functional Interpolation and Adaptive Networks
Broomhead D., Lowe D.., 1988
An efficient method for inverse dynamics of manipulators based on the virtual work principle
Zhang C.D., Song S.M.., 1993
V-REP: A versatile and scalable robot simulation framework
Rohmer E., Singh S.P.N., Freese M.., 0
A robotics toolbox for MATLAB
Corke P.., 1996
Identifying the Dynamic Model Used by the KUKA LWR: A Reverse Engineering Approach
Gaz C., Flacco F., Luca A.D.., 0
Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®
Quellen

PMID: 28208697
PubMed | Europe PMC

Suchen in

Google Scholar