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Found 7 papers, 2 papers with code
Vision-based Learning for Drones: A Survey
Drones as advanced cyber-physical systems are undergoing a transformative shift with the advent of vision-based learning, a field that is rapidly gaining prominence due to its profound impact on drone autonomy and functionality.
Target Search and Navigation in Heterogeneous Robot Systems with Deep Reinforcement Learning
Collaborative heterogeneous robot systems can greatly improve the efficiency of target search and navigation tasks.
EEG-based Sleep Staging with Hybrid Attention
Specifically, we propose a well-designed spatio-temporal attention mechanism to adaptively assign weights to inter-channels and intra-channel EEG segments based on the spatio-temporal relationship of the brain during different sleep stages.
An EEG Channel Selection Framework for Driver Drowsiness Detection via Interpretability Guidance
However, the raw EEG data is inherently noisy and redundant, which is neglected by existing works that just use single-channel EEG data or full-head channel EEG data for model training, resulting in limited performance of driver drowsiness detection.
Learning Multi-Pursuit Evasion for Safe Targeted Navigation of Drones
Safe navigation of drones in the presence of adversarial physical attacks from multiple pursuers is a challenging task.
Collaborative Target Search with a Visual Drone Swarm: An Adaptive Curriculum Embedded Multistage Reinforcement Learning Approach
Equipping drones with target search capabilities is highly desirable for applications in disaster rescue and smart warehouse delivery systems.
Foresight of Graph Reinforcement Learning Latent Permutations Learnt by Gumbel Sinkhorn Network
Vital importance has necessity to be attached to cooperation in multi-agent environments, as a result of which some reinforcement learning algorithms combined with graph neural networks have been proposed to understand the mutual interplay between agents.
