780 citations · 1.8k across the 45 of their papers we have counts for
12 papers · 1 filter
InteractionNet: Joint Planning and Prediction for Autonomous Driving with Transformers
Jiawei Fu, Yanqing Shen, Zhiqiang Jian +3
Planning and prediction are two important modules of autonomous driving and have experienced tremendous advancement recently. Nevertheless, most existing methods regard planning an…
CoBigICP: Robust and Precise Point Set Registration using Correntropy Metrics and Bidirectional Correspondence
Pengyu Yin, Di Wang, Shaoyi Du +3
In this paper, we propose a novel probabilistic variant of iterative closest point (ICP) dubbed as CoBigICP. The method leverages both local geometrical information and global nois…
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…
Visual Semantic SLAM with Landmarks for Large-Scale Outdoor Environment
Zirui Zhao, Yijun Mao, Yan Ding +2
Semantic SLAM is an important field in autonomous driving and intelligent agents, which can enable robots to achieve high-level navigation tasks, obtain simple cognition or reasoni…
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).…