no code implementations • 25 Jan 2024 • Jan Dohmen, Frank Röder, Manfred Eppe
One problem with researching cognitive modeling and reinforcement learning (RL) is that researchers spend too much time on setting up an appropriate computational framework for their experiments.
1 code implementation • 18 Nov 2022 • Frank Röder, Manfred Eppe
To evaluate our approach, we propose a collection of benchmark environments for action correction in language-conditioned reinforcement learning, utilizing a synthetic instructor to generate language goals and their corresponding corrections.
no code implementations • 18 Aug 2022 • Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D. H. Nguyen, Martin V. Butz, Stefan Wermter
According to cognitive psychology and related disciplines, the development of complex problem-solving behaviour in biological agents depends on hierarchical cognitive mechanisms.
Hierarchical Reinforcement Learning reinforcement-learning +1
2 code implementations • 8 Apr 2022 • Frank Röder, Manfred Eppe, Stefan Wermter
We show that hindsight instructions improve the learning performance, as expected.
no code implementations • 11 Mar 2021 • Manfred Eppe, Pierre-Yves Oudeyer
This paper outlines a perspective on the future of AI, discussing directions for machines models of human-like intelligence.
no code implementations • 18 Dec 2020 • Manfred Eppe, Christian Gumbsch, Matthias Kerzel, Phuong D. H. Nguyen, Martin V. Butz, Stefan Wermter
We then relate these insights with contemporary hierarchical reinforcement learning methods, and identify the key machine intelligence approaches that realise these mechanisms.
Hierarchical Reinforcement Learning reinforcement-learning +1
no code implementations • 25 Nov 2020 • Phuong D. H. Nguyen, Yasmin Kim Georgie, Ezgi Kayhan, Manfred Eppe, Verena Vanessa Hafner, Stefan Wermter
Safe human-robot interactions require robots to be able to learn how to behave appropriately in \sout{humans' world} \rev{spaces populated by people} and thus to cope with the challenges posed by our dynamic and unstructured environment, rather than being provided a rigid set of rules for operations.
no code implementations • 13 Nov 2020 • Phuong D. H. Nguyen, Manfred Eppe, Stefan Wermter
Cognitive science suggests that the self-representation is critical for learning and problem-solving.
no code implementations • 11 Nov 2020 • Thilo Fryen, Manfred Eppe, Phuong D. H. Nguyen, Timo Gerkmann, Stefan Wermter
Reinforcement learning is a promising method to accomplish robotic control tasks.
1 code implementation • 26 Sep 2020 • Matthias Kerzel, Fares Abawi, Manfred Eppe, Stefan Wermter
In this follow-up study, we expand the task and the model to reaching for objects in a three-dimensional space with a novel dataset based on augmented reality and a simulation environment.
1 code implementation • 7 May 2020 • Frank Röder, Manfred Eppe, Phuong D. H. Nguyen, Stefan Wermter
Hierarchical abstraction and curiosity-driven exploration are two common paradigms in current reinforcement learning approaches to break down difficult problems into a sequence of simpler ones and to overcome reward sparsity.
no code implementations • 23 May 2019 • Manfred Eppe, Phuong D. H. Nguyen, Stefan Wermter
In this article, we build on these novel methods to facilitate the integration of action planning with reinforcement learning by exploiting the reward-sparsity as a bridge between the high-level and low-level state- and control spaces.
no code implementations • 27 Sep 2018 • Pablo Barros, German I. Parisi, Manfred Eppe, Stefan Wermter
The model adapts concepts of expectation learning to enhance the unisensory representation based on the learned bindings.
no code implementations • 17 Sep 2018 • Manfred Eppe, Sven Magg, Stefan Wermter
Deep reinforcement learning has recently gained a focus on problems where policy or value functions are independent of goals.
no code implementations • 3 Jul 2018 • Manfred Eppe, Matthias Kerzel, Erik Strahl, Stefan Wermter
We present a novel approach for interactive auditory object analysis with a humanoid robot.
no code implementations • 26 Jul 2016 • Michael Spranger, Jakob Suchan, Mehul Bhatt, Manfred Eppe
This paper presents a computational model of the processing of dynamic spatial relations occurring in an embodied robotic interaction setup.
no code implementations • 22 Apr 2016 • Manfred Eppe, Sean Trott, Jerome Feldman
We develop a natural language interface for human robot interaction that implements reasoning about deep semantics in natural language.
no code implementations • 1 Mar 2014 • Manfred Eppe
We present an epistemic action theory for tractable epistemic reasoning as an extension to the h-approximation (HPX) theory.
no code implementations • 4 Jun 2013 • Manfred Eppe, Mehul Bhatt
Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems.
no code implementations • 17 Apr 2013 • Manfred Eppe, Mehul Bhatt, Frank Dylla
We propose an approximation of the Possible Worlds Semantics (PWS) for action planning.