We construct a shared latent space from different sensory modalities via contrastive learning.
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Bridging the gap between symbolic and connectionist paradigms via Graph Neural Network embeddings
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.
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.