There is a growing interest in using deep learning technologies within the manufacturing
industry to improve quality, productivity, safety, and efficiency, while also reducing costs and …

The grand aim of having a single robot that can manipulate arbitrary objects in diverse
settings is at odds with the paucity of robotics datasets. Acquiring and growing such datasets …

The proliferation of Large Language Models (LLMs) has s fueled a shift in robot learning
from automation towards general embodied Artificial Intelligence (AI). Adopting foundation …

Robotic vision applications often necessitate a wide range of visual perception tasks, such
as object detection, segmentation, and identification. While there have been substantial …

Visual tracking of objects is a fundamental technology for industry 4.0, allowing the
integration of digital content and real-world objects. The industrial operation known as …

Although the applications of artificial intelligence especially deep learning had greatly
improved various aspects of intelligent manufacturing, they still face challenges for wide …

In this paper, we address a recent trend in robotic home appliances to include vision
systems on personal devices, capable of personalizing the appliances on the fly. In …

As Embodied AI advances, it increasingly enables robots to handle the complexity of
household manipulation tasks more effectively. However, the application of robots in these …

One of the challenges of using Time-of-Flight (ToF) sensors for dimensioning objects is that
the depth information suffers from issues such as low resolution, self-occlusions, noise, and …

In the field of the intralogistics industry, we present DMZoomNet, a novel architecture that
combines deep learning-based detectors with distance information to enhance object …