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Learning Robust Latent Representation for Reinforcement Learning with Multi-Modal Observations

We construct a shared latent space from different sensory modalities via contrastive learning.

Enhancing Deep Learning with Symbolic Knowledge

Bridging the gap between symbolic and connectionist paradigms via Graph Neural Network embeddings

Discriminator Gradient Flow

Using Gradient Flows to Refine Samples from Deep Generative Models

Event-Driven Visual-Tactile Sensing and Learning

Accurate, Fast, and Low-powered Multi-Sensory Perception via Neuromorphic Sensing and Learning

Trust in Robots: Challenges and Opportunities

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.

TactileSGNet: A Spiking Graph Neural Network for Event-based Tactile Object Recognition

We propose a Spiking Graph Neural Network to take into account taxel geometry.