Getting to Know One Another Calibrating Intent, Capabilities and Trust for Human-Robot Collaboration
We address the problem of intention and capability calibration in human-robot collaboration with a decision-theoretic approach.
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We address the problem of intention and capability calibration in human-robot collaboration with a decision-theoretic approach.
We propose a Spiking Graph Neural Network to take into account taxel geometry.
We show that robots achieve fast classification of textures through Neural Encoding and Spiking Neural Network.
We present a new method for training GANs via characteristic functions
We study and model how trust and attention affect how humans interact with autonomous vehicles.
Harold was awarded the faculty teaching award for the year 19/20!
Training robots that can interactively assist humans with private information