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 leverage informative source distributions for imitation learning.

Arena 3.0 - Advancing Social Navigation in Collaborative and Highly Dynamic Environments

We introduce the third iteration of the Arena platform - Arena 3.0, a platform to develop, train, and benchmark social navigation approaches!

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.

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: ...

Out-of-Distribution Detection with a Single Unconditional Diffusion Model

We show that a single unconditional diffusion model performs competitively in out-of-distribution detection tasks by measuring the rate-of-change and curvature of diffusion paths connecting data samples to the standard normal distribution.

LTLDoG: Satisfying Temporally-Extended Symbolic Constraints for Safe Diffusion-based Planning

We develop a safe planning method for trajectory generation by sampling from diffusion model under different LTLf constraints.

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.

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.