3 accepted papers
In this paper, we present our submission to the EEG-Music Emotion Recognition Challenge at ICASSP 2025. Our work focused on Task 2, where the objective was to classify the emotional state of subjects in a discrete valence-arousal space while they listen to music. Our proposed solution adopts an ense…
Applying Reinforcement Learning to solve real-world optimization problems presents significant challenges because of the large amount of data normally required. A popular solution is to train the algorithms in a simulation and transfer the weights to the real system. However, sim-to-real approaches…
In this work, we propose a data-driven approach to optimize the parameters of a simulation such that control policies can be directly transferred from simulation to a real-world quadrotor. Our neural network-based policies take only onboard sensor data as input and run entirely on the embed-ded hard…