Collaborative, Learning, and Adaptive Robots Lab at NUS.
We Develop Physical and Social Skills for Trustworthy Robots.
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.
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.
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
Both our submitted papers were accepted to Neurips this year! Come join us in Detroit! Congrats to Alvin and Shuyue. Find out more about the papers below: ...
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: ...
Congratulations Sreejith on successfully defending his thesis! Sreejith’s work was on value alignment in human-centric AI/robots. Check out his Neurips pap...
Harold has been awarded an Early Career Spotlight at the Robotics: Science and Systems 2023. The Early Career Spotlight “* acknowledges the outstanding acc...
We study a model of multi-population learning with heterogenous beliefs.
We construct a decomposed latent state space model for perspective-taking for human robot interaction.
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.
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.
