Search Results for author: Jan Achterhold

Found 10 papers, 4 papers with code

Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation

no code implementations17 Sep 2024 Jan Achterhold, Suresh Guttikonda, Jens U. Kreber, Haolong Li, Joerg Stueckler

In this article, we propose a novel approach for learning a probabilistic, terrain- and robot-aware forward dynamics model (TRADYN) which can adapt to such variations and demonstrate its use for navigation.

Autonomous Navigation Friction +3

Attention Normalization Impacts Cardinality Generalization in Slot Attention

1 code implementation4 Jul 2024 Markus Krimmel, Jan Achterhold, Joerg Stueckler

Object-centric scene decompositions are important representations for downstream tasks in fields such as computer vision and robotics.

Image Segmentation Object +3

Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning

no code implementations11 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.

continuous-control Continuous Control +4

Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models

1 code implementation22 Feb 2021 Jan Achterhold, Joerg Stueckler

In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties.

reinforcement-learning Reinforcement Learning (RL)

Learning to Identify Physical Parameters from Video Using Differentiable Physics

no code implementations17 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.

Friction Predict Future Video Frames +2

Planning from Images with Deep Latent Gaussian Process Dynamics

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.

Gaussian Processes Transfer Learning

Variational Network Quantization

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.

Quantization Variational Inference

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