Grasping is the process of picking up an object by applying forces and torques at a set of
contacts. Recent advances in deep learning methods have allowed rapid progress in robotic …

Nowadays, robots are dominating the manufacturing, entertainment, and healthcare
industries. Robot vision aims to equip robots with the capabilities to discover information …

This paper presents a brief review of affordance research in robotics, with special
concentrations on its applications in grasping and manipulation of objects. The concept of …

A segmentation-based architecture is proposed to decompose objects into multiple primitive
shapes from monocular depth input for robotic manipulation. The backbone deep network is …

Affordance learning considers the interaction opportunities for an actor in the scene and thus
has wide application in scene understanding and intelligent robotics. In this paper, we focus …

Affordance detection, which refers to perceiving objects with potential action possibilities in
images, is a challenging task since the possible affordance depends on the person's …

In this work, we present a part-based grasp planning approach that is capable of generating
grasps that are applicable to multiple familiar objects. We show how object models can be …

The motivation of this paper is to develop an intelligent robot assembly system using multi-
modal vision for next-generation industrial assembly. The system includes two phases …

MacGyvering is defined as creating or repairing something in an inventive or improvised
way by utilizing objects that are available at hand. In this paper, we explore a subset of …

This work presents an approach for part-based grasp planning in point clouds. A complete
pipeline is proposed that allows a robot manipulator equipped with a range camera to …