arXiv:2310.19495
Deep Learning for Visual Navigation of Underwater Robots
원문 초록
This paper aims to briefly survey deep learning methods for visual navigation of underwater robotics. The scope of this paper includes the visual perception of underwater robotics with deep learning methods, the available visual underwater datasets, imitation learning, and reinforcement learning methods for navigation. Additionally, relevant works will be categorized under the imitation learning or deep learning paradigm for underwater robots for clarity of the training methodologies in the current landscape. Literature that uses deep learning algorithms to process non-visual data for underwater navigation will not be considered, except as contrasting examples.
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Structured Reinforcement Learning for Combinatorial Decision-Making
Reinforcement Learning via Implicit Imitation Guidance
SGN-CIRL: Scene Graph-based Navigation with Curriculum, Imitation, and Reinforcement Learning
Efficient Online RL Fine Tuning with Offline Pre-trained Policy Only
Fully Offline Reinforcement Learning
High-Throughput Distributed Reinforcement Learning via Adaptive Policy Synchronization
토론
이 논문과 연결된 포럼 글이 아직 없습니다.