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pro vyhledávání: '"Korthals, Timo"'
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 been successfully applied to many tasks in recent years, Deep Reinforce
Externí odkaz:
http://arxiv.org/abs/2005.11164
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 conclude our work with a comparison to curiosity-driven learning.
C
C
Externí odkaz:
http://arxiv.org/abs/1911.00584
Autor:
Korthals, Timo
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 Autoencoder (M$^2$VAE).
Comment: Appendix for the IEEE FUSION 2019 s
Comment: Appendix for the IEEE FUSION 2019 s
Externí odkaz:
http://arxiv.org/abs/1903.07303
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 following benefits: Detections can be exchanged effectively between r
Externí odkaz:
http://arxiv.org/abs/1809.04558
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 benefit from both advances, as they are in reality mobile driving fac
Externí odkaz:
http://arxiv.org/abs/1805.08595
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 automotive applications, are equipped with a variety of different sen
Externí odkaz:
http://arxiv.org/abs/1805.02944
Autor:
Lach, Luca, Korthals, Timo, Ferro, Francesco, Ritter, Helge, Schilling, Malte, Dorronsoro, Bernabé, Amodeo, Lionel, Pavone, Mario, Ruiz, Patricia
Publikováno v:
Communications in Computer and Information Science ISBN: 9783030856717
OLA
OLA
Variational Auto Encoder (VAE) provide an efficient latent space representation of complex data distributions which is learned in an unsupervised fashion. Using such a representation as input to Reinforcement Learning (RL) approaches may reduce learn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7a70e30b5773519135e432d23453e20c
https://pub.uni-bielefeld.de/record/2958668
https://pub.uni-bielefeld.de/record/2958668
Publikováno v:
BioRob
PUB-Publications at Bielefeld University
PUB-Publications at Bielefeld University
Deep Reinforcement Learning (DRL) approaches have shown tremendous success over the last years in different application areas. But control of robots in real world settings and when facing unpredictable environments has still proven to be a difficult
Autor:
Schöpping, Thomas, Korthals, Timo, Hesse, Marc, Rückert, Ulrich, Madani, Kurosh, Gusikhin, Oleg
Publikováno v:
ICINCO (2)
With the continuous progress in robotics and application of such systems in evermore scenarios, safety and flexibility become increasingly important aspects and new designs should thus emphasize real-time capability and modularity. This work points o