Existing grasp prediction approaches are mostly based on offline learning, while, ignoring
the exploratory grasp learning during online adaptation to new picking scenarios, ie, objects …

Grasping inhomogeneous objects in real-world applications remains a challenging task due
to the unknown physical properties such as mass distribution and coefficient of friction. In …

Existing grasp detection methods usually rely on data-driven strategies to learn grasping
features from labeled data, restricting their generalization to new scenes and objects …

The integration of language and vision for object affordance understanding is pivotal for the
advancement of embodied agents. Current approaches are often limited by reliance on …

The ability of a robot to pick an object, known as robot grasping, is crucial for several
applications, such as assembly or sorting. In such tasks, selecting the right target to pick is …

Grasping unknown objects without re-training is still a challenging task for robot grasping.
The traditional methods, including data-driven and transfer-learning based grasp methods …