Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Julian Viereck"'
Autor:
Avadesh Meduri, Paarth Shah, Julian Viereck, Majid Khadiv, Ioannis Havoutis, Ludovic Righetti
Publikováno v:
IEEE Transactions on Robotics (Submitted)
Online planning of whole-body motions for legged robots is challenging due to the inherent nonlinearity in the robot dynamics. In this work, we propose a nonlinear model predictive control (MPC) framework, the BiConMP which can generate whole body tr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f9a1c354568f57aad9086c19cef4770
Autor:
Maximilien Naveau, Andreas Krause, Julian Viereck, Andrii Zadaianchuk, Bernhard Schölkopf, Aditya Garg, Georg Martius, Philippe Wenk, Ludovic Righetti, Stefan Bauer, Diego Agudelo-España, Felix Grimminger, Joel Akpo, Manuel Wüthrich
Publikováno v:
ICRA
In the context of model-based reinforcement learning and control, a large number of methods for learning system dynamics have been proposed in recent years. The purpose of these learned models is to synthesize new control policies. An important open
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f81a95eecabe47e007bbd6a46aefc19b
https://doi.org/10.1109/icra40945.2020.9197392
https://doi.org/10.1109/icra40945.2020.9197392
Autor:
Ludovic Righetti, Julian Viereck
Publikováno v:
2021 IEEE International Conference on Robotics and Automation (ICRA)
ICRA
2021 IEEE-RAS International Conference on Robotics and Automation (ICRA)
ICRA
2021 IEEE-RAS International Conference on Robotics and Automation (ICRA)
Whole-body optimizers have been successful at automatically computing complex dynamic locomotion behaviors. However they are often limited to offline planning as they are computationally too expensive to replan with a high frequency. Simpler models a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f1c97b2f9b5a378b6882faad1c209868
Autor:
Felix Grimminger, Julian Viereck, Felix Widmaier, Ludovic Righetti, Majid Khadiv, Avadesh Meduri, Maximilien Naveau, Jonathan Fiene, Vincent Berenz, Manuel Wüthrich, Alexander Badri-Spröwitz, Thomas Flayols, Steve Heim
Publikováno v:
IEEE Robotics and Automation Letters
We present a new open-source torque-controlled legged robot system, with a low-cost and low-complexity actuator module at its core. It consists of a high-torque brushless DC motor and a low-gear-ratio transmission suitable for impedance and force con
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2fcedb7a98a75852630566e482cbd0e1
Publikováno v:
IEEE Robotics and Automation Letters
In this work we present a method for learning a reactive policy for a simple dynamic locomotion task involving hard impact and switching contacts where we assume the contact location and contact timing to be unknown. To learn such a policy, we use op
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::705ecf8eb03b17d7f90b42ffd62c3156