A Dexterous and Compliant (DexCo) Hand Based on Soft Hydraulic Actuation for Human-Inspired Fine In-Hand Manipulation

被引:0
|
作者
Zhou, Jianshu [1 ]
Huang, Junda [1 ]
Dou, Qi [2 ]
Abbeel, Pieter [3 ]
Liu, Yunhui [1 ]
机构
[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Peoples R China
[2] Chinese Univ Hong Kong, Dept Comp Sci & Engn, Hong Kong, Peoples R China
[3] Univ Calif Berkeley, Berkeley, CA 94720 USA
关键词
Robots; Robot sensing systems; Thumb; Complexity theory; Grasping; Sensors; Hydraulic systems; Grippers; Accuracy; Soft robotics; Grasping and manipulation; proprioception; robotic hand; soft actuation; soft robotics; GRIPPER; DESIGN; PINCH;
D O I
10.1109/TRO.2024.3508932
中图分类号
TP24 [机器人技术];
学科分类号
080202 ; 1405 ;
摘要
Human beings possess a remarkable skill for fine in-hand manipulation, utilizing both intrafinger interactions (in-finger) and finger-environment interactions across a wide range of daily tasks. These tasks range from skilled activities like screwing light bulbs, picking and sorting pills, and in-hand rotation, to more complex tasks such as opening plastic bags, cluttered bin picking, and counting cards. Despite its prevalence in human activities, replicating these fine motor skills in robotics remains a substantial challenge. This study tackles the challenge of fine in-hand manipulation by introducing the dexterous and compliant (DexCo) hand system. The DexCo hand mimics human dexterity, replicating the intricate interaction between the thumb, index, and middle fingers, with a contractable palm. The key to maneuverable fine in-hand manipulation lies in its innovative soft hydraulic actuation, which strikes a balance between control complexity, dexterity, compliance, and motion accuracy within a compact structure, enhancing the overall performance of the system. The model of soft hydraulic actuation, based on hydrostatic force analysis, reveals the compliance of hand joints, which is also further extended to a dedicated robot operating system (ROS) package for DexCo hand simulation, considering both motion and stiffness aspects. Dedicated velocity and position teleoperation controllers are designed for implementing real physical manipulation tasks. The benchmark results show that the fingertip achieves a maximum repeatable finger strength of 34.4 N, a grasp cycle time of less than 2.04 s, and a maximum repeatability accuracy of 0.03 mm. Experimental results demonstrate the DexCo hand successfully performs complex fine in-hand manipulation tasks, providing a promising solution for advancing robotic manipulation capabilities toward the human level.
引用
收藏
页码:666 / 686
页数:21
相关论文
共 22 条
  • [1] A Dexterous Soft Robotic Hand for Delicate In-Hand Manipulation
    Abondance, Sylvain
    Teeple, Clark B.
    Wood, Robert J.
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2020, 5 (04): : 5502 - 5509
  • [2] Dexterous Soft Hands Linearize Feedback-Control for In-Hand Manipulation
    Sieler, Adrian
    Brock, Oliver
    2023 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS), 2023, : 8757 - 8764
  • [3] BCL-13: A 13-DOF Soft Robotic Hand for Dexterous Grasping and In-Hand Manipulation
    Zhou, Jianshu
    Yi, Juan
    Chen, Xiaojiao
    Liu, Zixie
    Wang, Zheng
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2018, 3 (04): : 3379 - 3386
  • [4] Human-inspired feedback synergies for environmental interaction with a dexterous robotic hand
    Kent, Benjamin A.
    Engeberg, Erik D.
    BIOINSPIRATION & BIOMIMETICS, 2014, 9 (04)
  • [5] A Soft-Rigid Hybrid Gripper With Lateral Compliance and Dexterous In-Hand Manipulation
    Zhu, Wenpei
    Lu, Chenghua
    Zheng, Qule
    Fang, Zhonggui
    Che, Haichuan
    Tang, Kailuan
    Zhu, Mingchao
    Liu, Sicong
    Wang, Zheng
    IEEE-ASME TRANSACTIONS ON MECHATRONICS, 2023, 28 (01) : 104 - 115
  • [6] SEMG-Based Complex Human In-Hand Motion Recognition for Dexterous Robotic Manipulation
    Xue, Yaxu
    Ru, Feifei
    Du, Haojie
    Yin, Kaiyang
    Li, Pengfei
    Ju, Zhaojie
    IEEE Access, 2025, 13 : 51042 - 51053
  • [7] A Learning Scheme for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions
    Dwivedi, Anany
    Kwon, Yongje
    McDaid, Andrew J.
    Liarokapis, Minas
    IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, 2019, 27 (10) : 2205 - 2215
  • [8] On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions
    Kwon, Yongje
    Dwivedi, Anany
    McDaid, Andrew J.
    Liarokapis, Minas
    2018 40TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), 2018, : 1672 - 1675
  • [9] EMG Based Decoding of Object Motion in Dexterous, In-Hand Manipulation Tasks
    Dwivedi, Anany
    Kwon, Yongje
    McDaid, Andrew J.
    Liarokapis, Minas
    2018 7TH IEEE INTERNATIONAL CONFERENCE ON BIOMEDICAL ROBOTICS AND BIOMECHATRONICS (BIOROB2018), 2018, : 1025 - 1031
  • [10] A Soft Robotic Gripper With an Active Palm and Reconfigurable Fingers for Fully Dexterous In-Hand Manipulation
    Pagoli, Amir
    Chapelle, Frederic
    Corrales, Juan Antonio
    Mezouar, Youcef
    Lapusta, Yuri
    IEEE ROBOTICS AND AUTOMATION LETTERS, 2021, 6 (04) : 7706 - 7713