Adding new gestures for human-robot interaction using EEG-based reinforcement learning

Reinforcement learning applications have evolved rapidly throughout the last decade. Recently the DFKI successfully implemented a reinforcement learning (RL) algorithm that uses error potentials evoked by error recognition in the human brain for human-robot interaction. The algorithm makes it possible to map gesture inputs to robotic actions using error potentials as feedback (reward in RL).

New situations in real world applications could make it necessary to adapt the approach. The master thesis will focus on investigating approaches that make it possible to add gestures and actions, while retaining already learned knowledge. For evaluation of the developed algorithm data has been recorded. The first results will be presented.


Raum Seminarraum 117, Robert-Hooke-Str. 5 in Bremen

In der Regel sind die Vorträge Teil von Lehrveranstaltungsreihen der Universität Bremen und nicht frei zugänglich. Bei Interesse wird um Rücksprache mit dem Sekretariat unter sek-ric(at)dfki.de gebeten.

zuletzt geändert am 31.03.2023