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 …

This paper presents a comprehensive survey on vision-based robotic grasping. We
conclude three key tasks during vision-based robotic grasping, which are object localization …

Humans excel at acquiring knowledge through observation. For example, we can learn to
use new tools by watching demonstrations. This skill is fundamental for intelligent systems to …

With the rapid development of machine learning, its powerful function in the machine vision
field is increasingly reflected. The combination of machine vision and robotics to achieve the …

Accurate affordance detection and segmentation with pixel precision is an important piece in
many complex systems based on interactions, such as robots and assitive devices. We …

Abstract We introduce One-shot Open Affordance Learning (OOAL) where a model is trained
with just one example per base object category but is expected to identify novel objects and …

Despite the enormous progress and generalization in robotic grasping in recent years,
existing methods have yet to scale and generalize task-oriented grasping to the same …

Dexterous manipulation, especially dexterous grasping, is a primitive and crucial ability of
robots that allows the implementation of performing human-like behaviors. Deploying the …

Currently, task-oriented grasp detection approaches are mostly based on pixel-level
affordance detection and semantic segmentation. These pixel-level approaches heavily rely …

Robots operating in everyday environments need to effectively perceive, model, and infer
semantic properties of objects. Existing knowledge reasoning frameworks only model binary …