25 citations · 30 across the 11 of their papers we have counts for
11 papers
Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application
Giulio Montecchio, Benjamin Hartmann, Sven Reimann +3
The integration horizon during the training plays a critical role in Physics-Enhanced Neural Ordinary Differential Equations. We draw conclusions about horizon extension in the tra…
Joint identification of permanent magnet synchronous machine and inverter
Giulio Montecchio, Sven Reimann, Benjamin Hartmann +3
In electric drive modeling, identifying the magnetic flux maps is essential for predicting accurately the torque, parameterizing a controller for tracking the torque or creating a…
Learning a Terrain- and Robot-Aware Dynamics Model for Autonomous Mobile Robot Navigation
Jan Achterhold, Suresh Guttikonda, Jens U. Kreber +2
Mobile robots should be capable of planning cost-efficient paths for autonomous navigation. Typically, the terrain and robot properties are subject to variations. For instance, pro…
Attention Normalization Impacts Cardinality Generalization in Slot Attention
Markus Krimmel, Jan Achterhold, Joerg Stueckler
Object-centric scene decompositions are important representations for downstream tasks in fields such as computer vision and robotics. The recently proposed Slot Attention module,…
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model
Suresh Guttikonda, Jan Achterhold, Haolong Li +2
In autonomous navigation settings, several quantities can be subject to variations. Terrain properties such as friction coefficients may vary over time depending on the location of…
Black-Box vs. Gray-Box: A Case Study on Learning Table Tennis Ball Trajectory Prediction with Spin and Impacts
Jan Achterhold, Philip Tobuschat, Hao Ma +3
In this paper, we present a method for table tennis ball trajectory filtering and prediction. Our gray-box approach builds on a physical model. At the same time, we use data to lea…
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning
Jan Achterhold, Markus Krimmel, Joerg Stueckler
Problems which require both long-horizon planning and continuous control capabilities pose significant challenges to existing reinforcement learning agents. In this paper we introd…
Explore the Context: Optimal Data Collection for Context-Conditional Dynamics Models
Jan Achterhold, Joerg Stueckler
In this paper, we learn dynamics models for parametrized families of dynamical systems with varying properties. The dynamics models are formulated as stochastic processes condition…
Learning to Identify Physical Parameters from Video Using Differentiable Physics
Rama Krishna Kandukuri, Jan Achterhold, Michael Möller +1
Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. V…
Sample-efficient Cross-Entropy Method for Real-time Planning
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes +4
Trajectory optimizers for model-based reinforcement learning, such as the Cross-Entropy Method (CEM), can yield compelling results even in high-dimensional control tasks and sparse…
Planning from Images with Deep Latent Gaussian Process Dynamics
Nathanael Bosch, Jan Achterhold, Laura Leal-Taixé +1
Planning is a powerful approach to control problems with known environment dynamics. In unknown environments the agent needs to learn a model of the system dynamics to make plannin…