Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand

doi:10.1109/TCDS.2022.3203025

	Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand
	Duan, Haonan 3,4; Wang, Peng 1,2,3,4; Li, Yiming 3,4; Li, Daheng 3,4; Wei, Wei 3,4
刊名	IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
	2023-09-01
卷号	15 期号:3 页码:1224-1238
关键词	Anthropomorphic hand handovers human-robot interaction
ISSN号	2379-8920
DOI	10.1109/TCDS.2022.3203025
通讯作者	Wang, Peng(peng_wang@ia.ac.cn)
英文摘要	Human-robot interaction plays an important role in robots serving human production and life. Object handover between humans and robotics is one of the fundamental problems of human-robot interaction. The majority of current work uses parallel-jaw grippers as the end-effector device, which limits the ability of the robot to grab miscellaneous objects from human and manipulate them subsequently. In this article, we present a framework for human-to-robot dexterous handover using an anthropomorphic hand. The framework takes images captured by two cameras to complete handover scene understanding, grasp configurations prediction, and handover execution. To enable the robot to generalize to diverse delivered objects with miscellaneous shapes and sizes, we propose an anthropomorphic hand grasp network (AHG-Net), an end-to-end network that takes the single-view point clouds of the object as input and predicts the suitable anthropomorphic hand configurations with five different grasp taxonomies. To train our model, we build a large-scale data set with 1M hand grasp annotations from 5K single-view point clouds of 200 objects. We implement a handover system using a UR5 robot arm and HIT-DLR II anthropomorphic robot hand based on our presented framework, which can not only adapt to different human givers but generalize to diverse novel objects with various shapes and sizes. The generalizability, reliability, and robustness of our method are demonstrated on 15 different novel objects with arbitrary handover poses from frontal and lateral positions, a system ablation study, a grasp planner comparison, and a user study on 6 participants delivering 15 objects from two benchmark sets.
资助项目	National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[62006229] ; National Natural Science Foundation of China[61771471] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050106] ; InnoHK Project
WOS关键词	MOVEMENT PRIMITIVES ; GRASP
WOS研究方向	Computer Science ; Robotics ; Neurosciences & Neurology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:001089186500020
资助机构	National Natural Science Foundation of China ; Strategic Priority Research Program of Chinese Academy of Science ; InnoHK Project
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/54388]
专题	智能机器人系统研究
通讯作者	Wang, Peng
作者单位	1.Chinese Acad Sci, Hong Kong Inst Sci & Innovat, Ctr Artificial Intelligence & Robot, Hong Kong, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Duan, Haonan,Wang, Peng,Li, Yiming,et al. Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2023,15(3):1224-1238.
APA	Duan, Haonan,Wang, Peng,Li, Yiming,Li, Daheng,&Wei, Wei.(2023).Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,15(3),1224-1238.
MLA	Duan, Haonan,et al."Learning Human-to-Robot Dexterous Handovers for Anthropomorphic Hand".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 15.3(2023):1224-1238.