Learning Adaptive Policies for Autonomous Excavation Under Various Soil Conditions by Adversarial Domain Sampling-Reference-Cited by-同舟云学术

Learning Adaptive Policies for Autonomous Excavation Under Various Soil Conditions by Adversarial Domain Sampling

Published:2023-09 Issue:9 Volume:8 Page:5536-5543
ISSN:2377-3766
Container-title:IEEE Robotics and Automation Letters
language:
Short-container-title:IEEE Robot. Autom. Lett.

Author:

Osa Takayuki¹^ORCID,Osajima Naoto²,Aizawa Masanori³,Harada Tatsuya¹^ORCID

Affiliation:

1. University of Tokyo, Tokyo, Japan

2. Kyushu Institute of Technology, Fukuoka, Japan

3. Komatsu Ltd., Kanagawa, Japan

Funder

JST AIP Acceleration Research

Moonshot R&D

CREST

JSPS KAKENHI

University of Tokyo

Komatsu

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Control and Optimization,Computer Science Applications,Computer Vision and Pattern Recognition,Mechanical Engineering,Human-Computer Interaction,Biomedical Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/7083369/10185095/10187614.pdf?arnumber=10187614

Reference28 articles.

1. Legged locomotion in challenging terrains using egocentric vision;agarwal;Proc Conf Robot Learn,0

2. RMA: Rapid Motor Adaptation for Legged Robots

3. Scalable deep reinforcement learning for vision-based robotic manipulation;kalashnikov;Proc Conf Robot Learn,0

4. Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping

5. Efficient off-policy meta-reinforcement learning via probabilistic context variables;rakelly;Proc Int Conf Mach Learn,0

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Review of Data Mining Strategies by Data Type, with a Focus on Construction Processes and Health and Safety Management;International Journal of Environmental Research and Public Health;2024-06-26