5 accepted papers
Dexterous manipulation has received considerable attention in recent research. Predominantly, existing studies have concentrated on reinforcement learning methods to address the substantial degrees of freedom in hand movements. Nonetheless, these methods typically suffer from low efficiency and accu
Language plays a crucial role in robotic manipulation, particularly in facilitating complex tasks. Previous work primarily focused on two-finger manipulation. However, leveraging language to guide reinforcement learning for dexterous hands remains a challenge due to their high degrees of freedom. In
The problem of grasping objects using a multi-finger hand has received significant attention in recent years. However, it remains challenging to handle a large number of unfamiliar objects in real and cluttered environments. In this work, we propose a representation that can be effectively mapped to…
Human dexterity facilitates the quick identification of optimal grasp configurations and the precise exertion of force, regardless of the novelty of the objects encountered. Although previous research has primarily focused on the use of visual cues, this work introduces an innovative approach to dex
Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics. Current methods mainly focus on the temporal smoothness of the predicted grasp poses but few consider their semantic consistency. Consequently, the predicted grasps are not guar…