Collaborative, Learning, and Adaptive Robots Lab at NUS.
We Develop Physical and Social Skills for Trustworthy Robots.
We explore the potential of LLMs to act as zero-shot human models for HRI. We contribute an empirical study and case studies on a simulated table-clearing task and a new robot utensil-passing experiment.
We contribute an empirical study into the effectiveness of LLMs, specifically GPT-3.5 variants, for the task of natural language goal translation to PDDL.
Inspired by Social Projection Theory, we use the robot's self model to efficiently model humans.
Accurate, Fast, and Low-powered Multi-Sensory Perception via Neuromorphic Sensing and Learning
Xie Yaqi successfully defended her thesis and is now Dr. Xie. Congratulations Yaqi! You can find out more about Yaqi’s work on embedding symbolic knowledg...
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...
We develop a family of stable continuous-time neural state space-models.
We extend gradient flow methods to a variety of high-quality image synthesis tasks using a novel density ratio learning method.
Transfer source policies to a target reinforcement learning task with safety constraints using Successor Features.
We introduce a model of multi-population learning with heterogenous beliefs.
We examine the problem of observed adversaries for deep policies, where observations of other agents can hamper robot performance.
