activity
20242026
most citedPIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.RO2026

DynaMOMA: Instantaneous Prediction of Grasp Poses for Mobile Manipulation of Dynamic Objects

Zhinan Yu, Junyan Xu, Jiazhao Zhang +9

Mobile manipulation is a fundamental robotics task and has advanced rapidly in recent years, enabling robots to navigate, reach, and interact with objects in complex environments.…

cs.RO2026

PAIWorld: A 3D-Consistent World Foundation Model for Robotic Manipulation

Yuhang Huang, Xuan Lv, Junyan Xu +25

World foundation models (WFMs) are powerful simulators, yet they predominantly operate in a single-view setting and lack the multi-view 3D consistency required for robotic manipula…

cs.LG20251 cited

PIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation

Wenxuan Li, Hang Zhao, Zhiyuan Yu +4

While non-prehensile manipulation (e.g., controlled pushing/poking) constitutes a foundational robotic skill, its learning remains challenging due to the high sensitivity to comple…

cs.RO2024

LLM-enhanced Scene Graph Learning for Household Rearrangement

Wenhao Li, Zhiyuan Yu, Qijin She +5

The household rearrangement task involves spotting misplaced objects in a scene and accommodate them with proper places. It depends both on common-sense knowledge on the objective…

cs.CV20241 cited

Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes

Zhiyuan Yu, Zheng Qin, Lintao Zheng +1

Multi-instance point cloud registration estimates the poses of multiple instances of a model point cloud in a scene point cloud. Extracting accurate point correspondence is to the…