1 code implementation • 14 Aug 2020 • Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, Jan Achterhold, Joerg Stueckler, Michal Rolinek, Georg Martius
However, their sampling inefficiency prevents them from being used for real-time planning and control.
Model-based Reinforcement Learning reinforcement-learning +1
1 code implementation • L4DC 2020 • Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé, Jörg Stückler
We propose to learn a deep latent Gaussian process dynamics (DLGPD) model that learns low-dimensional system dynamics from environment interactions with visual observations.
no code implementations • ICLR 2018 • Jan Achterhold, Jan Mathias Koehler, Anke Schmeink, Tim Genewein
In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem.
no code implementations • 17 Sep 2020 • Rama Krishna Kandukuri, Jan Achterhold, Michael Möller, Jörg Stückler
Video prediction models often learn a latent representation of video which is encoded from input frames and decoded back into images.
1 code implementation • 22 Feb 2021 • Jan Achterhold, Joerg Stueckler
In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties.
no code implementations • 11 Jul 2022 • Jan Achterhold, Markus Krimmel, Joerg Stueckler
In this paper we introduce a novel hierarchical reinforcement learning agent which links temporally extended skills for continuous control with a forward model in a symbolic discrete abstraction of the environment's state for planning.
no code implementations • 24 May 2023 • Jan Achterhold, Philip Tobuschat, Hao Ma, Dieter Buechler, Michael Muehlebach, Joerg Stueckler
Our gray-box approach builds on a physical model.
no code implementations • 18 Jul 2023 • Suresh Guttikonda, Jan Achterhold, Haolong Li, Joschka Boedecker, Joerg Stueckler
Terrain properties such as friction coefficients may vary over time depending on the location of the robot.