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Learning Self-Supervised Traversability with Navigation Experiences of Mobile Robots: A Risk-Aware Self-Training Approach
Ikhyeon Cho
,
Woojin Chung
*
*
Corresponding author for this work
School of Mechanical Engineering
Research output
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Contribution to journal
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Article
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peer-review
6
Citations (Scopus)
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Dive into the research topics of 'Learning Self-Supervised Traversability with Navigation Experiences of Mobile Robots: A Risk-Aware Self-Training Approach'. Together they form a unique fingerprint.
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Keyphrases
Mobile Robot
100%
Training Approaches
100%
Self-training
100%
Navigation Experience
100%
Risk-aware
100%
Traversability
100%
Pseudo Label
50%
Neural Network
25%
Urban Environment
25%
Learning Methods
25%
High Risk
25%
Outdoor Environment
25%
Robot Navigation
25%
Rugged Terrain
25%
Weighting Scheme
25%
Real-world Experiment
25%
Risk-sensitive
25%
Driving Experience
25%
Training Scheme
25%
Degree of Difficulty
25%
Data Augmentation
25%
Difficult Terrain
25%
Traversability Estimation
25%
Instance Weighting
25%
Computer Science
Robot
100%
Mobile Robot
100%
Self-Supervised Learning
100%
Neural Network
50%
Robot Navigation
50%
Urban Environment
50%
Data Augmentation
50%
Driving Experience
50%