no code implementations • 12 Jan 2024 • Jan Schneider, Pierre Schumacher, Simon Guist, Le Chen, Daniel Häufle, Bernhard Schölkopf, Dieter Büchler
Policy gradient methods hold great potential for solving complex continuous control tasks.
no code implementations • 13 Sep 2023 • Jan Schneider, Pierre Schumacher, Daniel Häufle, Bernhard Schölkopf, Dieter Büchler
Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks.
no code implementations • 3 Mar 2023 • Simon Guist, Jan Schneider, Alexander Dittrich, Vincent Berenz, Bernhard Schölkopf, Dieter Büchler
Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort.
no code implementations • 8 Jul 2022 • Isabell Wochner, Pierre Schumacher, Georg Martius, Dieter Büchler, Syn Schmitt, Daniel F. B. Haeufle
Humans are able to outperform robots in terms of robustness, versatility, and learning of new tasks in a wide variety of movements.
1 code implementation • 30 May 2022 • Pierre Schumacher, Daniel Häufle, Dieter Büchler, Syn Schmitt, Georg Martius
Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous movements despite their vast amount of muscles.
1 code implementation • NeurIPS 2021 • Nico Gürtler, Dieter Büchler, Georg Martius
Hierarchical reinforcement learning (HRL) holds great potential for sample-efficient learning on challenging long-horizon tasks.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 10 Jun 2020 • Dieter Büchler, Simon Guist, Roberto Calandra, Vincent Berenz, Bernhard Schölkopf, Jan Peters
This work is the first to (a) fail-safe learn of a safety-critical dynamic task using anthropomorphic robot arms, (b) learn a precision-demanding problem with a PAM-driven system despite the control challenges and (c) train robots to play table tennis without real balls.
no code implementations • 7 Apr 2019 • Dieter Büchler, Roberto Calandra, Jan Peters
High-speed and high-acceleration movements are inherently hard to control.