activity
20182025
most citedRobotic Grasping from Classical to Modern: A Survey

22 citations · 60 across the 9 of their papers we have counts for

collaborators

10 papers

cs.LG2025

MInCo: Mitigating Information Conflicts in Distracted Visual Model-based Reinforcement Learning

Shiguang Sun, Hanbo Zhang, Zeyang Liu +4

Existing visual model-based reinforcement learning (MBRL) algorithms with observation reconstruction often suffer from information conflicts, making it difficult to learn compact r…

cs.RO2024★ 1 cited

DexDiff: Towards Extrinsic Dexterity Manipulation of Ungraspable Objects in Unrestricted Environments

Chengzhong Ma, Houxue Yang, Hanbo Zhang +5

Grasping large and flat objects (e.g. a book or a pan) is often regarded as an ungraspable task, which poses significant challenges due to the unreachable grasping poses. Previous…

cs.RO2022★ 22 cited

Robotic Grasping from Classical to Modern: A Survey

Hanbo Zhang, Jian Tang, Shiguang Sun +1

Robotic Grasping has always been an active topic in robotics since grasping is one of the fundamental but most challenging skills of robots. It demands the coordination of robotic…

cs.RO2021★ 2 cited

Density-based Curriculum for Multi-goal Reinforcement Learning with Sparse Rewards

Deyu Yang, Hanbo Zhang, Xuguang Lan +1

Multi-goal reinforcement learning (RL) aims to qualify the agent to accomplish multi-goal tasks, which is of great importance in learning scalable robotic manipulation skills. Howe…

cs.RO2020★ 4 cited

REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds

Binglei Zhao, Hanbo Zhang, Xuguang Lan +3

Reliable robotic grasping in unstructured environments is a crucial but challenging task. The main problem is to generate the optimal grasp of novel objects from partial noisy obse…

cs.RO2018★ 1 cited

A Real-time Robotic Grasp Approach with Oriented Anchor Box

Hanbo Zhang, Xinwen Zhou, Xuguang Lan +3

Grasp is an essential skill for robots to interact with humans and the environment. In this paper, we build a vision-based, robust and real-time robotic grasp approach with fully c…

cs.RO2018★ 6 cited

A Multi-task Convolutional Neural Network for Autonomous Robotic Grasping in Object Stacking Scenes

Hanbo Zhang, Xuguang Lan, Site Bai +3

Autonomous robotic grasping plays an important role in intelligent robotics. However, how to help the robot grasp specific objects in object stacking scenes is still an open proble…

cs.RO2018

ROI-based Robotic Grasp Detection for Object Overlapping Scenes

Hanbo Zhang, Xuguang Lan, Site Bai +3

Grasp detection with consideration of the affiliations between grasps and their owner in object overlapping scenes is a necessary and challenging task for the practical use of the…

cs.RO2018★ 22 cited

Fully Convolutional Grasp Detection Network with Oriented Anchor Box

Xinwen Zhou, Xuguang Lan, Hanbo Zhang +3

In this paper, we present a real-time approach to predict multiple grasping poses for a parallel-plate robotic gripper using RGB images. A model with oriented anchor box mechanism…

cs.RO2018★ 2 cited

Visual Manipulation Relationship Network

Hanbo Zhang, Xuguang Lan, Xinwen Zhou +3

Robotic grasping detection is one of the most important fields in robotics, in which great progress has been made recent years with the help of convolutional neural network (CNN).…