Bridging the gap between symbolic and connectionist paradigms via Graph Neural Network embeddings
Collaborative, Learning, and Adaptive Robots Lab at NUS.
We Develop Physical and Social Skills for Trustworthy Robots.
Accurate, Fast, and Low-powered Multi-Sensory Perception via Neuromorphic Sensing and Learning
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Our joint work with Desmond Ong, Jamil Zaki and Noah Goodman on Applying Probabilistic Programming to Affective Computing is one of 5 Best Papers (out of 82 ...
Kaiqi Chen is awarded the Research Achievement Award for his RSS 2022 paper on Differentiable Social Projection for Human Robot Communication.
Harold has been awarded the University Annual Teaching Excellence Award (2022). Harold also won the Faculty Teaching Excellence Award and is now on Faculty H...
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This paper proposes SCALES, a general framework that translates well-established fairness principles into a common representation based on CMDPs.
We develop an accurate physics-inspired model for describing how a population of Q-learning agents adapt as they interact.
Inspired by Social Projection Theory, we use the robot's self model to efficiently model humans.
We propose a deep switching state space model that can capture both state-dependent and time-dependent switching patterns in time series data.
We show that robots can extend their perception through grasped tools/objects via dynamic tactile sensing.
We embed symbolic knowledge expressed as linear temporal logic (LTL) and use these embeddings to guide the training of deep sequential models.