Christian Muhmann — accepted AI-conference papers

2025

Toward Dynamic Control of Tendon-driven Continuum Robots using Clarke Transform

Christian Muhmann, Reinhard M. Grassmann, Max Bartholdt, Jessica Burgner-Kahrs

IROS 2025

In this paper, we propose a dynamic model and control framework for tendon-driven continuum robots (TDCRs) with multiple segments and an arbitrary number of tendons per segment. Our approach leverages the Clarke transform, the Euler-Lagrange formalism, and the piecewise constant curvature assumption

Cited by 0 Source ↗Scholar ↗

2024

Learning-Based Nonlinear Model Predictive Control of Articulated Soft Robots Using Recurrent Neural Networks

Hendrik Schäfke, Tim-Lukas Habich, Christian Muhmann, Simon F. G. Ehlers, Thomas Seel, Moritz Schappler

RA-L 2024

Soft robots pose difficulties in terms of control, requiring novel strategies to effectively manipulate their compliant structures. Model-based approaches face challenges due to the high dimensionality and nonlinearities such as hysteresis effects. In contrast, learning-based approaches provide nonl

Cited by 10 Source ↗Scholar ↗