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

Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models

We apply techniques from continual learning to the problem of selective forgetting in deep generative models. Our method, dubbed Selective Amnesia, allows users to remap undesired concepts to user-defined ones.

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.

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

2 Papers at Neurips'23.

Both our submitted papers were accepted to Neurips this year! Come join us in New Orleans! Congrats to Alvin and Shuyue. Find out more about the papers below...

3 Papers at IROS'23.

CLeAR had three papers accepted to IROS this year! Come join us in Detroit! Congrats to Tasbolat, Bowen, and Kaiqi. Find out more about the papers below: ...

Introducing Dr. Sreejith Balakrishnan!

Congratulations Sreejith on successfully defending his thesis! Sreejith’s work was on value alignment in human-centric AI/robots. Check out his Neurips pap...

Early Career Spotlight at R:SS 2023!

Harold has been awarded an Early Career Spotlight at the Robotics: Science and Systems 2023. The Early Career Spotlight “* acknowledges the outstanding acc...

GRaCE: Optimizing Grasps to Satisfy Ranked Criteria in Complex Scenarios

We propose an optimization based grasp synthesis framework, GRaCE, to generate context-specific grasps in complex scenarios. We test GRaCE in a simulator and a real-world grasping tasks.

The Best of Both Worlds in Network Population Games: Reaching Consensus and Convergence to Equilibrium

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

Latent Emission-Augmented Perspective-Taking (LEAPT) for Human-Robot Interaction

We construct a decomposed latent state space model for perspective-taking for human robot interaction.

Refining 6-DoF Grasps with Context-Specific Classifiers

We propose a sampling-based grasp synthesis framework, GraspFlow, to generate context-specific grasps. We test GraspFlow in a real-world table-top grasping task.

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

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