RT info:eu-repo/semantics/article
T1 Optimization and improvement of a robotics gaze control system using LSTM networks
A1 Duque Domingo, Jaime
A1 Gómez García-Bermejo, Jaime
A1 Zalama Casanova, Eduardo
K1 Humanoid robots
K1 Robots humanoides
K1 Computer vision
K1 Visión artificial
K1 Recurrent neural networks
K1 Redes neuronales recurrentes
AB Gaze control represents an important issue in the interaction between a robot and humans. Specifically, deciding who to pay attention to in a multi-party conversation is one way to improve the naturalness of a robot in human-robot interaction. This control can be carried out by means of two different models that receive the stimuli produced by the participants in an interaction, either an on-center off-surround competitive network or a recurrent neural network. A system based on a competitive neural network is able to decide who to look at with a smooth transition in the focus of attention when significant changes in stimuli occur. An important aspect in this process is the configuration of the different parameters of such neural network. The weights of the different stimuli have to be computed to achieve human-like behavior. This article explains how these weights can be obtained by solving an optimization problem. In addition, a new model using a recurrent neural network with LSTM layers is presented. This model uses the same set of stimuli but does not require its weighting. This new model is easier to train, avoiding manual configurations, and offers promising results in robot gaze control. The experiments carried out and some results are also presented.
PB Springer Link
SN 1380-7501
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/48472
UL https://uvadoc.uva.es/handle/10324/48472
LA eng
NO Multimedia Tools and Applications, 2021. 18 p.
NO Producción Científica
DS UVaDOC
RD 22-nov-2024