3 citations · 3 across the 7 of their papers we have counts for
7 papers
Learning Panorama-Aware VLA for Mobile Manipulation with Whole-Body Teleoperation
Donglin Yang, Haoran Chen, Xingyu Chen +6
Mobile manipulation is a key capability for embodied intelligence, enabling robots to accomplish complex multi-stage tasks in open-world environments. However, mobile manipulation…
ArtPro: Self-Supervised Articulated Object Reconstruction with Adaptive Integration of Mobility Proposals
Xuelu Li, Zhaonan Wang, Xiaogang Wang +3
Reconstructing articulated objects into high-fidelity digital twins is crucial for applications such as robotic manipulation and interactive simulation. Recent self-supervised meth…
RobustVLA: On Robustness of Vision-Language-Action Model against Multi-Modal Perturbations
Jianing Guo, Zhenhong Wu, Chang Tu +13
In Vision-Language-Actionf(VLA) models, robustness to real-world perturbations is critical for deployment. Existing methods target simple visual disturbances, overlooking the broad…
Adaptive and Iterative Point Cloud Denoising with Score-Based Diffusion Model
Zhaonan Wang, Manyi Li, ShiQing Xin +1
Point cloud denoising task aims to recover the clean point cloud from the scanned data coupled with different levels or patterns of noise. The recent state-of-the-art methods often…
AGaussian: Anchor-Graph Structured Gaussian Splatting for Instance-Level 3D Scene Understanding and Editing
Zhaonan Wang, Manyi Li, Changhe Tu
3D Gaussian Splatting (3DGS) has witnessed exponential adoption across diverse applications, driving a critical need for semantic-aware 3D Gaussian representations to enable scene…
SymBridge: A Human-in-the-Loop Cyber-Physical Interactive System for Adaptive Human-Robot Symbiosis
Haoran Chen, Yiteng Xu, Yiming Ren +14
The development of intelligent robots seeks to seamlessly integrate them into the human world, providing assistance and companionship in daily life and work, with the ultimate goal…
Varying Manifolds in Diffusion: From Time-varying Geometries to Visual Saliency
Junhao Chen, Manyi Li, Zherong Pan +2
Deep generative models learn the data distribution, which is concentrated on a low-dimensional manifold. The geometric analysis of distribution transformation provides a better und…