activity
20232026
most citedPIN-WM: Learning Physics-INformed World Models for Non-Prehensile Manipulation

1 citations · 2 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

SituHOI: Scalable Capture of Scene-Situated Full-Body Human-Object Interaction

Qifan Wang, Qihang Fang, Boao Shi +4

Modeling human-object interaction (HOI) in real environments requires jointly representing whole-body motion, manipulated objects, and scene geometry, yet existing datasets trade o…

cs.RO2026

OmniClone: Engineering a Robust, All-Rounder Whole-Body Humanoid Teleoperation System

Yixuan Li, Le Ma, Yutang Lin +8

Whole-body humanoid teleoperation enables humans to remotely control humanoid robots, serving as both a real-time operational tool and a scalable engine for collecting demonstratio…

cs.RO2025

Learning Human-Humanoid Coordination for Collaborative Object Carrying

Yushi Du, Yixuan Li, Baoxiong Jia +5

Human-humanoid collaboration shows significant promise for applications in healthcare, domestic assistance, and manufacturing. While compliant robot-human collaboration has been ex…

cs.RO2025

VER: Vision Expert Transformer for Robot Learning via Foundation Distillation and Dynamic Routing

Yixiao Wang, Mingxiao Huo, Zhixuan Liang +8

Pretrained vision foundation models (VFMs) advance robotic learning via rich visual representations, yet individual VFMs typically excel only in specific domains, limiting generali…

cs.LG2025★ 1 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.CV2023

Learning Part Motion of Articulated Objects Using Spatially Continuous Neural Implicit Representations

Yushi Du, Ruihai Wu, Yan Shen +1

Articulated objects (e.g., doors and drawers) exist everywhere in our life. Different from rigid objects, articulated objects have higher degrees of freedom and are rich in geometr…

cs.CV2023★ 1 cited

Leveraging SE(3) Equivariance for Learning 3D Geometric Shape Assembly

Ruihai Wu, Chenrui Tie, Yushi Du +2

Shape assembly aims to reassemble parts (or fragments) into a complete object, which is a common task in our daily life. Different from the semantic part assembly (e.g., assembling…