5 citations · 8 across the 6 of their papers we have counts for
6 papers
Newtonian and Lagrangian Neural Networks: A Comparison Towards Efficient Inverse Dynamics Identification
Minh Trinh, Andreas René Geist, Josefine Monnet +3
Accurate inverse dynamics models are essential tools for controlling industrial robots. Recent research combines neural network regression with inverse dynamics formulations of the…
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…
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…
The Wheelbot: A Jumping Reaction Wheel Unicycle
A. René Geist, Jonathan Fiene, Naomi Tashiro +2
Combining off-the-shelf components with 3D-printing, the Wheelbot is a symmetric reaction wheel unicycle that can jump onto its wheels from any initial position. With non-holonomic…
Structured learning of rigid-body dynamics: A survey and unified view from a robotics perspective
A. René Geist, Sebastian Trimpe
Accurate models of mechanical system dynamics are often critical for model-based control and reinforcement learning. Fully data-driven dynamics models promise to ease the process o…
Learning Constrained Dynamics with Gauss Principle adhering Gaussian Processes
A. Rene Geist, Sebastian Trimpe
The identification of the constrained dynamics of mechanical systems is often challenging. Learning methods promise to ease an analytical analysis, but require considerable amounts…