3 papers
cs.RO2026
Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video
Henrik Krauss, Johann Licher, Naoya Takeishi +2
Learning soft continuum robot (SCR) dynamics from video offers flexibility but existing methods lack interpretability or rely on prior assumptions. Model-based approaches require p…
cs.RO2026
Adaptive Model-Predictive Control of a Soft Continuum Robot Using a Physics-Informed Neural Network Based on Cosserat Rod Theory
Johann Licher, Max Bartholdt, Henrik Krauss +3
Dynamic control of soft continuum robots (SCRs) holds great potential for expanding their applications, but remains a challenging problem due to the high computational demands of a…
cs.RO2026
Accurate Open-Loop Control of a Soft Continuum Robot Through Visually Learned Latent Representations
Henrik Krauss, Johann Licher, Naoya Takeishi +2
This work addresses open-loop control of a soft continuum robot (SCR) from video-learned latent dynamics. Visual Oscillator Networks (VONs) from previous work are used, that provid…