Fault-tolerant Learning
Fault-Tolerant Learning is a learning framework in safe robot learning that, unlike standard learning approaches, ensures continued performance and convergence in the presence of hardware failures, software errors, or unmodeled environmental disruptions by explicitly integrating fault detection, adaptation, and robustness mechanisms into the learning process.
192
Publications
8.3K
Citations
518
Authors
235
Institutions
Publications per year
2017–2026
102
Parents
192
Tieshan Li, Weiwei Bai, Qi Liu et al. · IEEE Transactions on Neural Networks and Learning Systems · 2021 · 196 citations
1–5 of 192
518
6
h-index
Hong Kong University of Science and Technology, Hong Kong SAR China
5 publications · 351 citations
5
h-index
Hong Kong University of Science and Technology, Hong Kong SAR China
5 publications · 223 citations
5
h-index
4
h-index
4
h-index
1–5 of 518
235
Tsinghua University
Beijing, China
9 h-index
19 publications
1.7K citations

Huazhong University of Science and Technology
Wuhan, China
6 h-index
15 publications
539 citations

City University of Hong Kong
Hong Kong, Hong Kong
6 h-index
13 publications
512 citations

Hong Kong University of Science and Technology
Hong Kong, Hong Kong
6 h-index
13 publications
1K citations
Liaoning Shihua University
Fushun, China
4 h-index
10 publications
461 citations
Northumbria University
Newcastle upon Tyne, United Kingdom
4 h-index
4 publications
131 citations

National Chung Hsing University
Taichung, Taiwan
4 h-index
4 publications
156 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
4 h-index
10 publications
545 citations
1–8 of 235
10 publications · 742 citations
9
h-index
7 publications · 288 citations
7
h-index
6 publications · 223 citations
6
h-index
5 publications · 285 citations
5
h-index
7 publications · 141 citations
5
h-index
1–5