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.
Posts
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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.
Fast texture classification using tactile neural coding and spiking neural network
We show that robots achieve fast classification of textures through Neural Encoding and Spiking Neural Network.
A characteristic function approach to deep implicit generative modeling
We present a new method for training GANs via characteristic functions
Modeling the Interplay of Trust and Attention in HRI an Autonomous Vehicle Study
We study and model how trust and attention affect how humans interact with autonomous vehicles.
Harold wins faculty teaching award for the 3rd time!
Harold was awarded the faculty teaching award for the year 19/20!