Event-Driven Visual-Tactile Sensing and Learning
Accurate, Fast, and Low-powered Multi-Sensory Perception via Neuromorphic Sensing and Learning
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Accurate, Fast, and Low-powered Multi-Sensory Perception via Neuromorphic Sensing and Learning
We examine how recent advances in psychometrics, trustworthy systems, deep learning etc. can help address challenges that arise with respect to trust in real-world human robot interactions.
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.