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Found 4 papers, 3 papers with code
Zero-Shot Reinforcement Learning on Graphs for Autonomous Exploration Under Uncertainty
An agent can use domain knowledge provided by human experts to learn efficiently.
Autonomous Exploration Under Uncertainty via Deep Reinforcement Learning on Graphs
We consider an autonomous exploration problem in which a range-sensing mobile robot is tasked with accurately mapping the landmarks in an a priori unknown environment efficiently in real-time; it must choose sensing actions that both curb localization uncertainty and achieve information gain.
Simulation-based Lidar Super-resolution for Ground Vehicles
We propose a methodology for lidar super-resolution with ground vehicles driving on roadways, which relies completely on a driving simulator to enhance, via deep learning, the apparent resolution of a physical lidar.
Robotics Image and Video Processing
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement Learning
Mapping and exploration of a priori unknown environments is a crucial capability for mobile robot autonomy.
