The intricate kinematics of the human hand enable simultaneous grasping and manipulation
of multiple objects, essential for tasks, such as object transfer and in-hand manipulation …

Humans use their dexterous fingers and adaptable palm in various multiobject grasping
strategies to efficiently move multiple objects together in various situations. Advanced …

Embodied learning for object-centric robotic manipulation is a rapidly developing and
challenging area in embodied AI. It is crucial for advancing next-generation intelligent robots …

Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes.
In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction cups …

Grasping is a fundamental skill for robots to interact with their environment. While grasp
execution requires coordinated movement of the hand and arm to achieve a collision-free …

Recent advances in AI have led to significant results in robotic learning, including natural
language-conditioned planning and efficient optimization of controllers using generative …

Autonomous grasping is a critical topic of robotic embodied intelligence. However, it remains
challenging for robots to grasp an intended target, particularly in cluttered and densely …

This letter introduces a novel Experience Forest algorithm designed for multi-object grasping
(MOG). Different from single-object grasping, for MOG, the hand poses of a few steps before …

Humans, this species expert in grasp detection, can grasp objects by taking into account
hand-object positioning information. This work proposes a method to enable a robot …

Developing robotic hands that adapt to real-world dynamics remains a fundamental
challenge in robotics and machine intelligence. Despite significant advances in replicating …