Luca M. Gambardella

Ground Robots,Training Set,Inertial Measurement Unit,Neural Network,Pose Estimation,Convolutional Neural Network,Deep Neural Network,Local Point,Pointing Gestures,Self-supervised Learning,Target Pose,Target Variable,Unmanned Aerial Vehicles,3D Space,Autonomous Navigation,Benchmark Instances,Conveyor Belt,Feasible Solution,Ground Truth Information,Human Pose Estimation,Legged Robots,Machine Learning,Mobile Robot,Perceptual Task,Power Consumption,Relative Pose,Robot Learning,Robot Motion,Robot Operating System,Search Space,Simultaneous Localization And Mapping,Spatial Perception Tasks,Target Location,Task Loss,Time Budget,Training Data,Training Dataset,Validation Set,World Frame,3D Orientation,3D Point,3D Pose,Additional Loss,Alternative Hypothesis,Amount Of Labels,Angular Speed,Anomaly Detection,Anomaly Detection Performance,Anomaly Score,Application Programming Interface,