CLeAR Lab

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

Octopi: Object Property Reasoning with Large Tactile-Language Models

We introduce PhysiCLeaR, an annotated dataset of everyday objects and tactile readings collected from a Gelsight Mini sensor, as well as Octopi, a system that leverages both tactile representation learning and large vision-language models to perform physical reasoning and inference, given tactile videos of multiple objects.

Don’t Start from Scratch: Behavioral Refinement via Interpolant-based Policy Diffusion

We develop interpolant policies that diffuse actions from informative source distributions.

GRaCE: Balancing Multiple Criteria to Achieve Stable, Collision-Free, and Functional Grasps

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.

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.

Harold delivers ICRA'24 Keynote

Harold gave a well-received Keynote at ICRA’24 on our group’s work on generative modeling and robotics! He spoke about two of our recent works on new kinds o...

4 Papers at R:SS'24.

All four of our submissions (3 papers, 1 demo) were accepted to R:SS 2024! A fantastic accomplishment by our CLeAR members Kaiqi, Samson, Tasbolat, Linh, Kel...

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: ...

Probable Object Location (POLo) Score Estimation for Efficient Object Goal Navigation

We introduce a novel framework centered around the Probable Object Location (POLo) score, which allows the agent to make data-driven decisions for efficient object search.

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.