no code implementations • 3 Oct 2023 • Jin Cheng, Marin Vlastelica, Pavel Kolev, Chenhao Li, Georg Martius
We demonstrate the effectiveness of our method on a local navigation task where a quadruped robot needs to reach the target within a finite horizon.
no code implementations • 15 Aug 2023 • Nico Gürtler, Felix Widmaier, Cansu Sancaktar, Sebastian Blaes, Pavel Kolev, Stefan Bauer, Manuel Wüthrich, Markus Wulfmeier, Martin Riedmiller, Arthur Allshire, Qiang Wang, Robert McCarthy, Hangyeol Kim, Jongchan Baek, Wookyong Kwon, Shanliang Qian, Yasunori Toshimitsu, Mike Yan Michelis, Amirhossein Kazemipour, Arman Raayatsanati, Hehui Zheng, Barnabas Gavin Cangan, Bernhard Schölkopf, Georg Martius
For this reason, a large part of the reinforcement learning (RL) community uses simulators to develop and benchmark algorithms.
2 code implementations • 28 Jul 2023 • Nico Gürtler, Sebastian Blaes, Pavel Kolev, Felix Widmaier, Manuel Wüthrich, Stefan Bauer, Bernhard Schölkopf, Georg Martius
To coordinate the efforts of the research community toward tackling this problem, we propose a benchmark including: i) a large collection of data for offline learning from a dexterous manipulation platform on two tasks, obtained with capable RL agents trained in simulation; ii) the option to execute learned policies on a real-world robotic system and a simulation for efficient debugging.
no code implementations • 21 Jul 2023 • Marin Vlastelica, Jin Cheng, Georg Martius, Pavel Kolev
There has been significant recent progress in the area of unsupervised skill discovery, utilizing various information-theoretic objectives as measures of diversity.
no code implementations • 16 Sep 2022 • Chenhao Li, Sebastian Blaes, Pavel Kolev, Marin Vlastelica, Jonas Frey, Georg Martius
Learning diverse skills is one of the main challenges in robotics.
no code implementations • 3 Sep 2020 • Vincenzo Bonifaci, Enrico Facca, Frederic Folz, Andreas Karrenbauer, Pavel Kolev, Kurt Mehlhorn, Giovanna Morigi, Golnoosh Shahkarami, Quentin Vermande
We formulate network design as the problem of constructing a network that efficiently supports a multi-commodity flow problem.
no code implementations • 16 Jul 2018 • Frank Ban, Vijay Bhattiprolu, Karl Bringmann, Pavel Kolev, Euiwoong Lee, David P. Woodruff
On the algorithmic side, for $p \in (0, 2)$, we give the first $(1+\epsilon)$-approximation algorithm running in time $n^{\text{poly}(k/\epsilon)}$.
no code implementations • NeurIPS 2017 • Karl Bringmann, Pavel Kolev, David Woodruff
For small $\psi$, our approximation factor is $1+o(1)$.
no code implementations • 30 Oct 2017 • Karl Bringmann, Pavel Kolev, David P. Woodruff
For small $\psi$, our approximation factor is $1+o(1)$.