4 accepted papers
The rapid evolution of retail robotics is set to transform in-store operations through advanced automation, spanning vision-based inventory tracking, order picking, packing, and restocking. Yet fine-grained product identification remains a bottleneck: assortments change, packaging evolves, and shelv…
Robots operating in everyday environments encounter a wide variety of previously unseen objects. Deep Learning methods simplify unknown object and scene segmentation by structuring inherent real-world complexities, improving visual scene understanding. However, they need vast amounts of labeled high
Robotic applications like automated order picking in warehouses or retail stores, or fetch and carry tasks in hospitals, care homes, or households rely on the capability of service robots to find and handle a specific type of object. These applications are challenging as the set of objects is very l…
Object instance detection is a highly relevant task to several robotic applications such as automated order picking, or household and hospital assistance robots. In these applications, a holistic scene labeling is often not required whereas it is sufficient to find a certain object type of interest,…