80 citations · 692 across the 61 of their papers we have counts for
4 papers · 1 filter
How to Train Your Differentiable Filter
Alina Kloss, Georg Martius, Jeannette Bohg
In many robotic applications, it is crucial to maintain a belief about the state of a system, which serves as input for planning and decision making and provides feedback during ta…
Self-supervised Visual Reinforcement Learning with Object-centric Representations
Andrii Zadaianchuk, Maximilian Seitzer, Georg Martius
Autonomous agents need large repertoires of skills to act reasonably on new tasks that they have not seen before. However, acquiring these skills using only a stream of high-dimens…
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…
Deep Graph Matching via Blackbox Differentiation of Combinatorial Solvers
Michal Rolínek, Paul Swoboda, Dominik Zietlow +3
Building on recent progress at the intersection of combinatorial optimization and deep learning, we propose an end-to-end trainable architecture for deep graph matching that contai…