9 citations · 9 across the 5 of their papers we have counts for
5 papers
Dual Iterative Learning Control for Multiple-Input Multiple-Output Dynamics with Validation in Robotic Systems
Jan-Hendrik Ewering, Alessandro Papa, Simon F. G. Ehlers +2
Solving motion tasks autonomously and accurately is a core ability for intelligent real-world systems. To achieve genuine autonomy across multiple systems and tasks, key challenges…
Bayesian Inference and Learning in Nonlinear Dynamical Systems: A Framework for Incorporating Explicit and Implicit Prior Knowledge
Björn Volkmann, Jan-Hendrik Ewering, Michael Meindl +2
Accuracy and generalization capabilities are key objectives when learning dynamical system models. To obtain such models from limited data, current works exploit prior knowledge an…
Efficient Online Inference and Learning in Partially Known Nonlinear State-Space Models by Learning Expressive Degrees of Freedom Offline
Jan-Hendrik Ewering, Björn Volkmann, Simon F. G. Ehlers +2
Intelligent real-world systems critically depend on expressive information about their system state and changing operation conditions, e.g., due to variation in temperature, locati…
Autonomous Iterative Motion Learning (AI-MOLE) of a SCARA Robot for Automated Myocardial Injection
Michael Meindl, Raphael Mönkemöller, Thomas Seel
Stem cell therapy is a promising approach to treat heart insufficiency and benefits from automated myocardial injection which requires highly precise motion of a robotic manipulato…
AI-MOLE: Autonomous Iterative Motion Learning for Unknown Nonlinear Dynamics with Extensive Experimental Validation
Michael Meindl, Simon Bachhuber, Thomas Seel
This work proposes Autonomous Iterative Motion Learning (AI-MOLE), a method that enables systems with unknown, nonlinear dynamics to autonomously learn to solve reference tracking…