Label-Free Adaptive Gaussian Sample Consensus Framework for Learning From Perfect and Imperfect Demonstrations-Reference-Cited by-同舟云学术

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Label-Free Adaptive Gaussian Sample Consensus Framework for Learning From Perfect and Imperfect Demonstrations

Published:2024-08 Issue:3 Volume:6 Page:1093-1103
ISSN:2576-3202
Container-title:IEEE Transactions on Medical Robotics and Bionics
language:
Short-container-title:IEEE Trans. Med. Robot. Bionics

Author:

Hu Yi¹^ORCID,Samadikhoshkho Zahra²^ORCID,Jin Jun¹,Tavakoli Mahdi³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

2. Department of Mechanical Engineering, University of Alberta, Edmonton, AB, Canada

3. Department of Electrical and Computer Engineering, and the Department of Biomedical Engineering, University of Alberta, Edmonton, AB, Canada

Funder

Canada Foundation for Innovation

Natural Sciences and Engineering Research Council (NSERC) of Canada

Canadian Institutes of Health Research

Alberta Jobs, Economy and Innovation Ministry’s Major Initiatives Fund to the Center for Autonomous Systems in Strengthening Future Communities

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx8/8253409/10631864/10582909.pdf?arnumber=10582909

Reference35 articles.

1. Robot Learning from Demonstration in Robotic Assembly: A Survey

2. Learning driving styles for autonomous vehicles from demonstration

3. Learning by Demonstration for Planning Activities of Daily Living in Rehabilitation and Assistive Robotics

4. Autonomous Bi-Manual Surgical Suturing Based on Skills Learned from Demonstration

5. Learning from demonstrations with partially observable task parameters

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