Zobrazeno 1 - 10
of 92
pro vyhledávání: '"KERZEL, MATTHIAS"'
Publikováno v:
In Artificial Neural Networks and Machine Learning -- ICANN 2024 (pp. 285--298). Springer Nature Switzerland
Causal learning allows humans to predict the effect of their actions on the known environment and use this knowledge to plan the execution of more complex actions. Such knowledge also captures the behaviour of the environment and can be used for its
Externí odkaz:
http://arxiv.org/abs/2410.07751
The state of an object reflects its current status or condition and is important for a robot's task planning and manipulation. However, detecting an object's state and generating a state-sensitive plan for robots is challenging. Recently, pre-trained
Externí odkaz:
http://arxiv.org/abs/2406.09988
Humanoid robots can benefit from their similarity to the human shape by learning from humans. When humans teach other humans how to perform actions, they often demonstrate the actions, and the learning human imitates the demonstration to get an idea
Externí odkaz:
http://arxiv.org/abs/2404.07735
Imitation can allow us to quickly gain an understanding of a new task. Through a demonstration, we can gain direct knowledge about which actions need to be performed and which goals they have. In this paper, we introduce a new approach to imitation l
Externí odkaz:
http://arxiv.org/abs/2401.08381
The paper introduces CycleIK, a neuro-robotic approach that wraps two novel neuro-inspired methods for the inverse kinematics (IK) task, a Generative Adversarial Network (GAN), and a Multi-Layer Perceptron architecture. These methods can be used in a
Externí odkaz:
http://arxiv.org/abs/2307.11554
Multimodal integration is a key component of allowing robots to perceive the world. Multimodality comes with multiple challenges that have to be considered, such as how to integrate and fuse the data. In this paper, we compare different possibilities
Externí odkaz:
http://arxiv.org/abs/2307.08471
Autor:
Kerzel, Matthias, Allgeuer, Philipp, Strahl, Erik, Frick, Nicolas, Habekost, Jan-Gerrit, Eppe, Manfred, Wermter, Stefan
Publikováno v:
Published in IEEE Access 2023
Robotic platforms that can efficiently collaborate with humans in physical tasks constitute a major goal in robotics. However, many existing robotic platforms are either designed for social interaction or industrial object manipulation tasks. The des
Externí odkaz:
http://arxiv.org/abs/2305.08528
Autor:
Özdemir, Ozan, Kerzel, Matthias, Weber, Cornelius, Lee, Jae Hee, Hafez, Muhammad Burhan, Bruns, Patrick, Wermter, Stefan
Publikováno v:
Applied Artificial Intelligence Volume 37, 2023 - Issue 1
Human infant learning happens during exploration of the environment, by interaction with objects, and by listening to and repeating utterances casually, which is analogous to unsupervised learning. Only occasionally, a learning infant would receive a
Externí odkaz:
http://arxiv.org/abs/2301.03353
Autor:
Lee, Jae Hee, Sioutis, Michael, Ahrens, Kyra, Alirezaie, Marjan, Kerzel, Matthias, Wermter, Stefan
Knowledge about space and time is necessary to solve problems in the physical world: An AI agent situated in the physical world and interacting with objects often needs to reason about positions of and relations between objects; and as soon as the ag
Externí odkaz:
http://arxiv.org/abs/2211.15566
The act of reaching for an object is a fundamental yet complex skill for a robotic agent, requiring a high degree of visuomotor control and coordination. In consideration of dynamic environments, a robot capable of autonomously adapting to novel situ
Externí odkaz:
http://arxiv.org/abs/2210.07851