GeNIE for In-the-Wild Navigation
We present a robust navigation framework designed for global deployment that achieved first place in the Earth Rover Challenge at ICRA 2025.
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.
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.
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2 Papers at R:SS'26.
We are excited to share that two papers from CLeAR were accepted to R:SS 2026! More information about the papers is coming soon, but here’s a snapshot: ...
3 Papers at ICLR'26.
All three of our submissions were accepted to ICLR 2026! A fantastic accomplishment by our CLeAR members and collaborators. Here’s a snapshot: Know ...
Latest Papers
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Arena 5.0 – A Photorealistic ROS2 Simulation Framework for Social Navigation with NVIDIA Isaac Gym
We introduce Arena 5.0, the fifth iteration of the Arena platform, featuring photorealistic simulation via NVIDIA Isaac Gym, comprehensive benchmarking, and customizable scenario generation!
Arena 4.0 - A Comprehensive ROS2 Development and Benchmarking Platform for Human-centric Navigation Using Generative-Model-based Environment Generation
We introduce the fourth iteration of the Arena platform - Arena 4.0, a platform to develop, train, and benchmark social navigation approaches with generative model–based environment generation!
DISCO: Language-Guided Manipulation with Diffusion Policies and Constrained Inpainting
DISCO uses vision-language models to guide diffusion policies with optimized keyframe inpainting, enabling superior zero-shot open-vocabulary robotic manipulation.
Coupled Hierarchical Diffusion for Long-Horizon Tasks
We propose CHD, a unified diffusion-based framework that tightly couples sub-goal and trajectory generation to improve long-horizon task planning.
VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback
We present VLA-Touch, a framework for VLA models with dual-level tactile feedback for contact-rich manipulation.
Diffusion Meets Options: Hierarchical Generative Skill Composition for Temporally-Extended Tasks
DOPPLER is a new framework that combines diffusion models and hierarchical reinforcement learning to let robots plan and replan complex, long-horizon tasks from offline data with robustness in the real world.
