Enhancing learning capabilities of movement primitives under distributed probabilistic framework for flexible assembly tasks

被引:2
|
作者
Wang, Likun [1 ]
Jia, Shuya [2 ]
Wang, Guoyan [3 ]
Turner, Alison [1 ]
Ratchev, Svetan [1 ]
机构
[1] Univ Nottingham, Ctr Aerosp Mfg, Nottingham, England
[2] Bauman Moscow State Tech Univ, Sch Comp Sci & Syst, Moscow, Russia
[3] Safran Landing Syst, Mfg Dept, Gloucester, England
来源
NEURAL COMPUTING & APPLICATIONS | 2023年 / 35卷 / 32期
关键词
Learning from demonstration; Task-parametrised; Probabilistic distributed framework; Bayesian committee machine; Assembly;
D O I
10.1007/s00521-021-06543-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper presents a novel probabilistic distributed framework based on movement primitives for flexible robot assembly. Since the modern advanced industrial cell usually deals with various scenarios that are not fixed via-point trajectories but highly reconfigurable tasks, the industrial robots used in these applications must be capable of adapting and learning new in-demand skills without programming experts. Therefore, we propose a probabilistic framework that could accommodate various learning abilities trained with different movement-primitive datasets, separately. Derived from the Bayesian Committee Machine, this framework could infer new adapting trajectories with weighted contributions of each training dataset. To verify the feasibility of our proposed imitation learning framework, the simulation comparison with the state-of-the-art movement learning framework task-parametrised GMM is conducted. Several key aspects, such as generalisation capability, learning accuracy and computation expense, are discussed and compared. Moreover, two real-world experiments, i.e. riveting picking and nutplate picking, are further tested with the YuMi collaborative robot to verify the application feasibility in industrial assembly manufacturing.
引用
收藏
页码:23453 / 23464
页数:12
相关论文
共 50 条
  • [31] A Flexible Distributed Optimization Framework for Service of Concurrent Tasks in Processing Networks
    Shi, Zai
    Eryilmaz, Atilla
    IEEE CONFERENCE ON COMPUTER COMMUNICATIONS (IEEE INFOCOM 2019), 2019, : 1072 - 1080
  • [32] Learning Insertion Primitives with Discrete-Continuous Hybrid Action Space for Robotic Assembly Tasks
    Zhang, Xiang
    Jin, Shiyu
    Wang, Changhao
    Zhu, Xinghao
    Tomizuka, Masayoshi
    2022 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION, ICRA 2022, 2022, : 9881 - 9887
  • [33] Continuous and Incremental Learning in physical Human-Robot Cooperation using Probabilistic Movement Primitives
    Schale, Daniel
    Stoelen, Martin F.
    Kyrkjebo, Erik
    2022 31ST IEEE INTERNATIONAL CONFERENCE ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION (IEEE RO-MAN 2022), 2022, : 1216 - 1223
  • [34] Adaptation of Bimanual Assembly Tasks using Iterative Learning Framework
    Likar, Nejc
    Nemec, Bojan
    Zlajpah, Leon
    Ando, Shingo
    Ude, Ales
    2015 IEEE-RAS 15TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS), 2015, : 771 - 776
  • [35] Guided Robot Skill Learning: A User-Study on Learning Probabilistic Movement Primitives with Non-Experts
    Knaust, Moritz
    Koert, Dorothea
    PROCEEDINGS OF THE 2020 IEEE-RAS 20TH INTERNATIONAL CONFERENCE ON HUMANOID ROBOTS (HUMANOIDS 2020), 2021, : 514 - 521
  • [36] Using Probabilistic Movement Primitives in Analyzing Human Motion Differences Under Transcranial Current Stimulation
    Xue, Honghu
    Herzog, Rebecca
    Berger, Till M.
    Baeumer, Tobias
    Weissbach, Anne
    Rueckert, Elmar
    FRONTIERS IN ROBOTICS AND AI, 2021, 8
  • [37] Hybrid Trajectory and Force Learning of Complex Assembly Tasks: A Combined Learning Framework
    Wang, Yan
    Beltran-Hernandez, Cristian C.
    Wan, Weiwei
    Harada, Kensuke
    IEEE Access, 2021, 9 : 60175 - 60186
  • [38] Hybrid Trajectory and Force Learning of Complex Assembly Tasks: A Combined Learning Framework
    Wang, Yan
    Beltran-Hernandez, Cristian C.
    Wan, Weiwei
    Harada, Kensuke
    IEEE ACCESS, 2021, 9 : 60175 - 60186
  • [39] GreedW: A Flexible and Efficient Decentralized Framework for Distributed Machine Learning
    Wang, Ting
    Jiang, Xin
    Li, Qin
    Cai, Haibin
    IEEE TRANSACTIONS ON COMPUTERS, 2024, 73 (03) : 801 - 814
  • [40] Enhancing the learning and supervision framework for training in flexible endoscopic evaluation of swallowing
    Robinson, H. Fiona
    CURRENT OPINION IN OTOLARYNGOLOGY & HEAD AND NECK SURGERY, 2021, 29 (03): : 204 - 212