6 papers
Decentralized Deep Reinforcement Learning for a Distributed and Adaptive Locomotion Controller of a Hexapod Robot
Malte Schilling, Kai Konen, Frank W. Ohl +1
Locomotion is a prime example for adaptive behavior in animals and biological control principles have inspired control architectures for legged robots. While machine learning has b…
A Perceived Environment Design using a Multi-Modal Variational Autoencoder for learning Active-Sensing
Timo Korthals, Malte Schilling, Jürgen Leitner
This contribution comprises the interplay between a multi-modal variational autoencoder and an environment to a perceived environment, on which an agent can act. Furthermore, we co…
MVAE - Derivation of a Multi-Modal Variational Autoencoder Objective from the Marginal Joint Log-Likelihood
Timo Korthals
This work gives an in-depth derivation of the trainable evidence lower bound obtained from the marginal joint log-Likelihood with the goal of training a Multi-Modal Variational Aut…
Coordinated Heterogeneous Distributed Perception based on Latent Space Representation
Timo Korthals, Jürgen Leitner, Ulrich Rückert
We investigate a reinforcement approach for distributed sensing based on the latent space derived from multi-modal deep generative models. Our contribution provides insights to the…
Towards Inverse Sensor Mapping in Agriculture
Timo Korthals, Mikkel Kragh, Peter Christiansen +1
In recent years, the drive of the Industry 4.0 initiative has enriched industrial and scientific approaches to build self-driving cars or smart factories. Agricultural applications…
Path Evaluation via HMM on Semantical Occupancy Grid Maps
Timo Korthals, Julian Exner, Thomas Schöpping +1
Traditional approaches to mapping of environments in robotics make use of spatially discretized representations, such as occupancy grid maps. Modern systems, e.g. in agriculture or…