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A Landmark-Aware Visual Navigation Dataset
However, recent advancements in the visual navigation field face challenges due to the lack of human datasets in the real world for efficient supervised representation learning of the environments.
Feudal Networks for Visual Navigation
We introduce a new approach to visual navigation using feudal learning, which employs a hierarchical structure consisting of a worker agent, a mid-level manager, and a high-level manager.
Hierarchical Reinforcement Learning for Temporal Pattern Prediction
In this work, we explore the use of hierarchical reinforcement learning (HRL) for the task of temporal sequence prediction.
Vision-Based Cranberry Crop Ripening Assessment
Agricultural domains are being transformed by recent advances in AI and computer vision that support quantitative visual evaluation.
Learning a Pedestrian Social Behavior Dictionary
Understanding pedestrian behavior patterns is a key component to building autonomous agents that can navigate among humans.
Feudal Steering: Hierarchical Learning for Steering Angle Prediction
The steering angle at a particular time instance is the worker network output which is regulated by the manager's high level task.
