382 citations · 801 across the 48 of their papers we have counts for
16 papers · 1 filter
Inferring Versatile Behavior from Demonstrations by Matching Geometric Descriptors
Niklas Freymuth, Nicolas Schreiber, Philipp Becker +2
Humans intuitively solve tasks in versatile ways, varying their behavior in terms of trajectory-based planning and for individual steps. Thus, they can easily generalize and adapt…
Deep Black-Box Reinforcement Learning with Movement Primitives
Fabian Otto, Onur Celik, Hongyi Zhou +3
\Episode-based reinforcement learning (ERL) algorithms treat reinforcement learning (RL) as a black-box optimization problem where we learn to select a parameter vector of a contro…
On Uncertainty in Deep State Space Models for Model-Based Reinforcement Learning
Philipp Becker, Gerhard Neumann
Improved state space models, such as Recurrent State Space Models (RSSMs), are a key factor behind recent advances in model-based reinforcement learning (RL). Yet, despite their em…
FusionVAE: A Deep Hierarchical Variational Autoencoder for RGB Image Fusion
Fabian Duffhauss, Ngo Anh Vien, Hanna Ziesche +1
Sensor fusion can significantly improve the performance of many computer vision tasks. However, traditional fusion approaches are either not data-driven and cannot exploit prior kn…
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
Oleg Arenz, Philipp Dahlinger, Zihan Ye +2
Variational inference with Gaussian mixture models (GMMs) enables learning of highly tractable yet multi-modal approximations of intractable target distributions with up to a few h…
MV6D: Multi-View 6D Pose Estimation on RGB-D Frames Using a Deep Point-wise Voting Network
Fabian Duffhauss, Tobias Demmler, Gerhard Neumann
Estimating 6D poses of objects is an essential computer vision task. However, most conventional approaches rely on camera data from a single perspective and therefore suffer from o…