An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications-Reference-Cited by-同舟云学术

An Independently Learnable Hierarchical Model for Bilateral Control-Based Imitation Learning Applications

Published:2022 Issue: Volume:10 Page:32766-32781
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hayashi Kazuki¹^ORCID,Sakaino Sho²^ORCID,Tsuji Toshiaki³^ORCID

Affiliation:

1. Faculty of Engineering, Information and Systems, University of Tsukuba, Tsukuba, Japan

2. Department of Intelligent Interaction Technologies, University of Tsukuba, Tsukuba, Japan

3. Department of Electrical and Electronic Systems, Saitama University, Saitama, Japan

Funder

Adaptable and Seamless Technology Transfer Program through the Target-Driven Research and Development (A-STEP) from the Japan Science and Technology Agency

Japan Society for the Promotion of Science by the Grant-in-Aid for Scientific Research

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09722836.pdf?arnumber=9722836

Reference43 articles.

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2. Covariate shift adaptation by importance weighted cross validation;sugiyama;J Mach Learn Res,2017

3. Emergence of Functional Hierarchy in a Multiple Timescale Neural Network Model: A Humanoid Robot Experiment

4. A New Autoregressive Neural Network Model with Command Compensation for Imitation Learning Based on Bilateral Control

5. Motion Generation Using Bilateral Control-Based Imitation Learning With Autoregressive Learning

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