Learning Robust Latent Representation for Reinforcement Learning with Multi-Modal Observations
We construct a shared latent space from different sensory modalities via contrastive learning.
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We construct a shared latent space from different sensory modalities via contrastive learning.
Bridging the gap between symbolic and connectionist paradigms via Graph Neural Network embeddings
Using Gradient Flows to Refine Samples from Deep Generative Models
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.