A Hierarchical Robot Learning Framework for Manipulator Reactive Motion Generation via Multi-Agent Reinforcement Learning and Riemannian Motion Policies-Reference-Cited by-同舟云学术

A Hierarchical Robot Learning Framework for Manipulator Reactive Motion Generation via Multi-Agent Reinforcement Learning and Riemannian Motion Policies

Published:2023 Issue: Volume:11 Page:126979-126994
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Wang Yuliu¹^ORCID,Sagawa Ryusuke¹^ORCID,Yoshiyasu Yusuke²^ORCID

Affiliation:

1. Intelligent and Mechanical Interaction Systems Program, University of Tsukuba, Ibaraki, Tsukuba, Japan

2. Computer Vision Research Team, Artificial Intelligence Research Center, National Institute of Advanced Industrial Science and Technology, Ibaraki, Tsukuba, Japan

Funder

JST SPRING

Japan Society for the Promotion of Science

NEDO (New Energy and Industrial Technology Development Organization) in Japan

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/ielx7/6287639/10005208/10283841.pdf?arnumber=10283841

Reference46 articles.

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2. RMP2: A structured composable policy class for robot learning;li;arXiv 2103 05922,2021

3. Multi-agent reinforcement learning: A survey;lucian;Proc 9th Int Conf Control Autom Robot Vis,2006

4. RMPflow: A Geometric Framework for Generation of Multitask Motion Policies

5. Redundant Robot Control Using Multi Agent Reinforcement Learning

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