4 accepted papers
In domestic environments, robots require a comprehensive understanding of their surroundings to interact effectively and intuitively with untrained humans. In this paper, we propose DVEFormer – an efficient RGB-D Transformer-based approach that predicts dense text-aligned visual embeddings (DVE) via
With the emergence of collaborative robots (cobots), human-robot collaboration in industrial manufacturing is coming into focus. For a cobot to act autonomously and as an assistant, it must understand human actions during assembly. To effectively train models for this task, a dataset containing suit…
As the application scenarios of mobile robots are getting more complex and challenging, scene understanding becomes increasingly crucial. A mobile robot that is supposed to operate autonomously in indoor environments must have precise knowledge about what objects are present, where they are, what th…
Automated grasping of arbitrary objects is an essential skill for many applications such as smart manufacturing and human robot interaction. This makes grasp detection a vital skill for automated robotic systems. Recent work in model-free grasp detection uses point cloud data as input and typically…