Recently human–robot collaboration (HRC) has emerged as a promising paradigm for mass
personalization in manufacturing owing to the potential to fully exploit the strength of human …

Continual learning is essential for all real-world applications, as frozen pre-trained models
cannot effectively deal with non-stationary data distributions. The purpose of this study is to …

Deep learning-based predictive quality enables manufacturing companies to make data-
driven predictions of the quality of a produced product based on process data. A central …

In this paper, we consider a challenging but realistic continual learning problem, Few-Shot
Continual Active Learning (FoCAL), where a CL agent is provided with unlabeled data for a …

Significant progress has been made recently in developing few-shot object segmentation
methods. Learning is shown to be successful in few-shot segmentation settings, using pixel …

For many applications, robots will need to be incrementally trained to recognize the specific
objects needed for an application. This paper presents a practical system for incrementally …

For robots to perform assistive tasks in unstructured home environments, they must learn
and reason on the semantic knowledge of the environments. Despite a resurgence in the …

The emergence of large vision models (LVMs) is following in the footsteps of the recent
prosperity of Large Language Models (LLMs) in following years. However, there'sa …

For successful deployment of robots in multifaceted situations, an understanding of the robot
for its environment is indispensable. With advancing performance of state-of-the-art object …

The material attribute of an object's surface is critical to enable robots to perform dexterous
manipulations or actively interact with their surrounding objects. Tactile sensing has shown …