Please use this identifier to cite or link to this item: http://dx.doi.org/10.18419/opus-14618
Authors: Vukelić, Mathias
Bui, Michael
Vorreuther, Anna
Lingelbach, Katharina
Title: Combining brain-computer interfaces with deep reinforcement learning for robot training : a feasibility study in a simulation environment
Issue Date: 2023
metadata.ubs.publikation.typ: Zeitschriftenartikel
metadata.ubs.publikation.seiten: 16
metadata.ubs.publikation.source: Frontiers in neuroergonomics 4 (2023), No. 1274730
URI: http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-146378
http://elib.uni-stuttgart.de/handle/11682/14637
http://dx.doi.org/10.18419/opus-14618
ISSN: 2673-6195
Abstract: Deep reinforcement learning (RL) is used as a strategy to teach robot agents how to autonomously learn complex tasks. While sparsity is a natural way to define a reward in realistic robot scenarios, it provides poor learning signals for the agent, thus making the design of good reward functions challenging. To overcome this challenge learning from human feedback through an implicit brain-computer interface (BCI) is used. We combined a BCI with deep RL for robot training in a 3-D physical realistic simulation environment. In a first study, we compared the feasibility of different electroencephalography (EEG) systems (wet- vs. dry-based electrodes) and its application for automatic classification of perceived errors during a robot task with different machine learning models. In a second study, we compared the performance of the BCI-based deep RL training to feedback explicitly given by participants. Our findings from the first study indicate the use of a high-quality dry-based EEG-system can provide a robust and fast method for automatically assessing robot behavior using a sophisticated convolutional neural network machine learning model. The results of our second study prove that the implicit BCI-based deep RL version in combination with the dry EEG-system can significantly accelerate the learning process in a realistic 3-D robot simulation environment. Performance of the BCI-based trained deep RL model was even comparable to that achieved by the approach with explicit human feedback. Our findings emphasize the usage of BCI-based deep RL methods as a valid alternative in those human-robot applications where no access to cognitive demanding explicit human feedback is available.
Appears in Collections:07 Fakultät Konstruktions-, Produktions- und Fahrzeugtechnik

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