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Rui Heng Yang

8 accepted papers

2026

CAPE: Context-Aware Diffusion Policy Via Proximal Mode Expansion for Collision Avoidance

Rui Heng Yang, Xuan Zhao, Léo Maxime Brunswic, Montgomery Tucker Alban, Matéo Clémente, Tongtong Cao +2
ICRA 2026poster

In robotics, diffusion models can capture multi-modal trajectories from demonstrations, making them a transformative approach in imitation learning. However, achieving optimal performance following this regiment requires a large-scale dataset, which is costly to obtain, especially for challenging ta…

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2026

HIPPo: Harnessing Image-To-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao +4
ICRA 2026poster

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is…

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2026

Improving Robotic Manipulation Robustness Via NICE Scene Surgery

Sajjad Pakdamansavoji, Mozhgan Pourkeshavarz, Adam Sigal, Zhiyuan Li, Rui Heng Yang, Amir Rasouli
ICRA 2026poster

Learning robust visuomotor policies for robotic manipulation remains a challenge in real-world settings, where visual distractors can significantly degrade performance and safety. In this work, we propose an effective and scalable framework, Naturalistic Inpainting for Context Enhancement (NICE). Ou…

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2025

Ergodic Generative Flows

Leo Maxime Brunswic, Mateo Clémente, Rui Heng Yang, Adam Sigal, Amir Rasouli, Yinchuan Li
ICML 2025poster

Generative Flow Networks (GFNs) were initially introduced on directed non-acyclic graphs to sample from an unnormalized distribution density. Recent works have extended the theoretical framework for generative methods allowing more flexibility and enhancing application range. However, many challenge…

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2025

HIPPo: Harnessing Image-to-3D Priors for Model-Free Zero-Shot 6D Pose Estimation

Yibo Liu, Zhaodong Jiang, Binbin Xu, Guile Wu, Yuan Ren, Tongtong Cao +4
RA-L 2025

This work focuses on the problem of 6D pose estimation for novel objects when a reference 3D model or posed reference images are not available. While existing methods can estimate the precise 6D pose of objects, they heavily rely on curated CAD models or reference images, the preparation of which is

Cited by 4Source ↗Scholar ↗
2025

RA-DP: Rapid Adaptive Diffusion Policy for Training-Free High-frequency Robotics Replanning

Xi Ye, Rui Heng Yang, Jun Jin, Yinchuan Li, Amir Rasouli
IROS 2025

Diffusion models exhibit impressive scalability in robotic task learning, yet they struggle to adapt to novel, highly dynamic environments. This limitation primarily stems from their constrained replanning ability: they either operate at a low frequency due to a time-consuming iterative sampling pro

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2025

Two-Steps Diffusion Policy for Robotic Manipulation via Genetic Denoising

Mateo Clémente, Leo Maxime Brunswic, Rui Heng Yang, Xuan Zhao, Yasser H. Khalil, Haoyu LEI +2
NeurIPS 2025poster

Diffusion models, such as diffusion policy, have achieved state-of-the-art results in robotic manipulation by imitating expert demonstrations. While diffusion models were originally developed for vision tasks like image and video generation, many of their inference strategies have been directly tran…

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2023

DenseShift: Towards Accurate and Efficient Low-Bit Power-of-Two Quantization

Xinlin Li, Bang Liu, Rui Heng Yang, Vanessa Courville, Chao Xing, Vahid Partovi Nia
ICCV 2023poster

Efficiently deploying deep neural networks on low-resource edge devices is challenging due to their ever-increasing resource requirements. To address this issue, researchers have proposed multiplication-free neural networks, such as Power-of-Two quantization, or also known as Shift networks, which a…

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