J
Julian Viereck
Researcher at Max Planck Society
Publications - 11
Citations - 272
Julian Viereck is an academic researcher from Max Planck Society. The author has contributed to research in topics: Computer science & Robot. The author has an hindex of 5, co-authored 9 publications receiving 113 citations. Previous affiliations of Julian Viereck include New York University.
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Journal ArticleDOI
An Open Torque-Controlled Modular Robot Architecture for Legged Locomotion Research
Felix Grimminger,Avadesh Meduri,Majid Khadiv,Julian Viereck,Manuel Wüthrich,Maximilien Naveau,Vincent Berenz,Steve Heim,Felix Widmaier,Thomas Flayols,Jonathan Fiene,Alexander Badri-Spröwitz,Ludovic Righetti +12 more
TL;DR: A novel controller which combines feedforward contact forces computed from a kino-dynamic optimizer with impedance control of the center of mass and base orientation is presented, which can regulate complex motions while being robust to environmental uncertainty.
Posted Content
TriFinger: An Open-Source Robot for Learning Dexterity.
Manuel Wüthrich,Felix Widmaier,Felix Grimminger,Joel Akpo,Shruti Joshi,Vaibhav Agrawal,Bilal Hammoud,Majid Khadiv,Miroslav Bogdanovic,Vincent Berenz,Julian Viereck,Maximilien Naveau,Ludovic Righetti,Bernhard Schölkopf,Stefan Bauer +14 more
TL;DR: The proposed open-source robotic platform is inexpensive, robust, and capable of complex interaction with external objects, and the software framework is largely robot-agnostic and can be used independently of the hardware proposed herein.
Journal ArticleDOI
BiConMP: A Nonlinear Model Predictive Control Framework for Whole Body Motion Planning
TL;DR: The BiConMP is used to generate various cyclic gaits on a real quadruped robot and its performance is evaluated on different terrain, countering unforeseen pushes and transitioning online between different gaits.
Journal ArticleDOI
Learning a Structured Neural Network Policy for a Hopping Task
TL;DR: In this article, the authors propose a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where they assume the contact location and contact timing to be unknown.
Posted Content
Learning a Centroidal Motion Planner for Legged Locomotion
Julian Viereck,Ludovic Righetti +1 more
TL;DR: The approach enables to learn with few training samples dynamic motions that can be used in a complete whole-body control framework at high frequency, which is usually not attainable with typical full-body optimizers.