As there come to be more applications of intelligent robots, their task object is becoming
more varied. However, it is still a challenge for a robot to handle unfamiliar objects. We …

In this paper, we present a modular robotic system to tackle the problem of generating and
performing antipodal robotic grasps for unknown objects from the n-channel image of the …

In order to complete many complex operations and attain more general-purpose utility,
robotic grasp is a necessary skill to master. As the most common essential action of robots in …

Task-relevant grasping is critical for industrial assembly, where downstream manipulation
tasks constrain the set of valid grasps. Learning how to perform this task, however, is …

Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that
enable subsequent manipulation tasks. To model the complex relationships between …

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 …

We propose a dual-module robotic system to tackle the problem of generating and
performing antipodal robotic grasps for unknown objects from the n-channel image of the …

Robotic grasping is a challenging task due to the diversity of object shapes. A sufficiently
labeled dataset is essential for the grasp pose detection methods based on deep learning …

Semantic grasping is the problem of selecting stable grasps that are functionally suitable for
specific object manipulation tasks. In order for robots to effectively perform object …

Over the last decade, the use of robots in production and daily life has increased. With
increasingly complex tasks and interaction in different environments including humans …