Collaborative, Learning, and Adaptive Robots Lab at NUS.
We Develop Physical and Social Skills for Trustworthy Robots.

Large Language Models as Zero-Shot Human Models for Human-Robot Interaction

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.

Translating Natural Language to Planning Goals with Large-Language Models

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.

MIRROR for Assistive Human-Robot Communication

Inspired by Social Projection Theory, we use the robot's self model to efficiently model humans.

Event-Driven Visual-Tactile Sensing and Learning

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

Introducing Dr. Xie Yaqi.

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...

IEEE T-AFFC Best Paper Award!

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 wins the 2022 Research Achievement Award.

Kaiqi Chen is awarded the Research Achievement Award for his RSS 2022 paper on Differentiable Social Projection for Human Robot Communication.

Harold wins NUS ATEA 2022.

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...

Neural Continuous-Discrete State Space Models for Irregularly-Sampled Time Series

We develop a family of stable continuous-time neural state space-models.

Generative Modeling with Flow-Guided Density Ratio Learning

We extend gradient flow methods to a variety of high-quality image synthesis tasks using a novel density ratio learning method.

Safety-Constrained Policy Transfer with Successor Features

Transfer source policies to a target reinforcement learning task with safety constraints using Successor Features.

Heterogeneous Beliefs and Multi-Population Learning in Network Games

We introduce a model of multi-population learning with heterogenous beliefs.

Observed Adversaries in Deep Reinforcement Learning

We examine the problem of observed adversaries for deep policies, where observations of other agents can hamper robot performance.