Most research on robot manipulation focuses on objects that are light enough for the robot to
pick them up. However, in our daily life, some objects are too big or too heavy to be picked …

With the emergence of onboard vision processing for areas such as the internet of things
(IoT), edge computing and autonomous robots, there is increasing demand for …

For the RoboCup Soccer AdultSize League the humanoid robot Sweaty uses a single fully
convolutional neural network to detect and localize the ball, opponents and other features …

Deep learning has revolutionised many fields, but it is still challenging to transfer its success
to small mobile robots with minimal hardware. Specifically, some work has been done to this …

We present a dataset specifically designed to be used as a benchmark to compare vision
systems in the RoboCup Humanoid Soccer domain. The dataset is composed of a collection …

How can there be Human-In-the-Loop-Learning (HILL) if datasets aimed at building
classifiers have ever more dimensions? We make two contributions. First, we examine the …

This paper proposes an enhancement of convolutional neural networks for object detection
in resource-constrained robotics through a geometric input transformation called Visual …

Deep Learning has become exceptionally popular in the last few years due to its success in
computer vision [1–3] and other fields of AI [4–6]. However, deep neural networks are …

The goal of this study is to test two different computing platforms with respect to their
suitability for running deep networks as part of a humanoid robot software system. One of the …

Soccer ball detection is identified as one of the critical challenges in the RoboCup
competition. It requires an efficient vision system capable of handling the task of detection …