3 papers
cs.RO2024
Pseudo-rigid body networks: learning interpretable deformable object dynamics from partial observations
Shamil Mamedov, A. René Geist, Jan Swevers +1
Accurately predicting deformable linear object (DLO) dynamics is challenging, especially when the task requires a model that is both human-interpretable and computationally efficie…
cs.RO2024
Safe Imitation Learning of Nonlinear Model Predictive Control for Flexible Robots
Shamil Mamedov, Rudolf Reiter, Seyed Mahdi Basiri Azad +4
Flexible robots may overcome some of the industry's major challenges, such as enabling intrinsically safe human-robot collaboration and achieving a higher payload-to-mass ratio. Ho…
cs.RO2024
Learning deformable linear object dynamics from a single trajectory
Shamil Mamedov, A. René Geist, Ruan Viljoen +2
The manipulation of deformable linear objects (DLOs) via model-based control requires an accurate and computationally efficient dynamics model. Yet, data-driven DLO dynamics models…