22 citations · 86 across the 20 of their papers we have counts for
12 papers · 1 filter
Prioritized Planning for Target-Oriented Manipulation via Hierarchical Stacking Relationship Prediction
Zewen Wu, Jian Tang, Xingyu Chen +3
In scenarios involving the grasping of multiple targets, the learning of stacking relationships between objects is fundamental for robots to execute safely and efficiently. However…
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…
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…
INVIGORATE: Interactive Visual Grounding and Grasping in Clutter
Hanbo Zhang, Yunfan Lu, Cunjun Yu +3
This paper presents INVIGORATE, a robot system that interacts with human through natural language and grasps a specified object in clutter. The objects may occlude, obstruct, or ev…
Probabilistic Human Motion Prediction via A Bayesian Neural Network
Jie Xu, Xingyu Chen, Xuguang Lan +1
Human motion prediction is an important and challenging topic that has promising prospects in efficient and safe human-robot-interaction systems. Currently, the majority of the hum…
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter
Hanbo Zhang, Deyu Yang, Han Wang +4
Despite the impressive progress achieved in robotic grasping, robots are not skilled in sophisticated tasks (e.g. search and grasp a specified target in clutter). Such tasks involv…
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…
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…
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…
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…
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…
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).…