2 citations · 5 across the 11 of their papers we have counts for
11 papers · 1 filter
GCNGrasp-VP: Affordance-Guided View Planning for Efficient Task-Oriented Grasping
Zanjia Tong, Wenlong Dong, Chengjie Zhang +1
Task-oriented grasping performance degrades significantly when object views suffer from occlusions. Existing task-oriented grasping methods typically assume task-relevant regions a…
VLAConf: Calibrated Task-Success Confidence for Vision-Language-Action Models
Dehao Huang, Aoxiang Gu, Chengjie Zhang +5
Task-success confidence estimation for Vision-Language-Action (VLA) models provides a crucial task-level signal for monitoring manipulation in open-world environments and supportin…
Grasp as You Dream: Imitating Functional Grasping from Generated Human Demonstrations
Chao Tang, Jiacheng Xu, Haofei Lu +4
Building generalist robots capable of performing functional grasping in everyday, open-world environments remains a significant challenge due to the vast diversity of objects and t…
Easy-IIL: Reducing Human Operational Burden in Interactive Imitation Learning via an Assistant Expert
Chengjie Zhang, Chao Tang, Wenlong Dong +3
Interactive Imitation Learning (IIL) typically relies on extensive human involvement for both offline demonstration and online interaction. Prior work primarily focuses on reducing…
HGDiffuser: Efficient Task-Oriented Grasp Generation via Human-Guided Grasp Diffusion Models
Dehao Huang, Wenlong Dong, Chao Tang +1
Task-oriented grasping (TOG) is essential for robots to perform manipulation tasks, requiring grasps that are both stable and compliant with task-specific constraints. Humans natur…
RTAGrasp: Learning Task-Oriented Grasping from Human Videos via Retrieval, Transfer, and Alignment
Wenlong Dong, Dehao Huang, Jiangshan Liu +2
Task-oriented grasping (TOG) is crucial for robots to accomplish manipulation tasks, requiring the determination of TOG positions and directions. Existing methods either rely on co…
FoundationGrasp: Generalizable Task-Oriented Grasping with Foundation Models
Chao Tang, Dehao Huang, Wenlong Dong +2
Task-oriented grasping (TOG), which refers to synthesizing grasps on an object that are configurationally compatible with the downstream manipulation task, is the first milestone t…
Commonsense Scene Graph-based Target Localization for Object Search
Wenqi Ge, Chao Tang, Hong Zhang
Object search is a fundamental skill for household robots, yet the core problem lies in the robot's ability to locate the target object accurately. The dynamic nature of household…
Efficient Object Rearrangement via Multi-view Fusion
Dehao Huang, Chao Tang, Hong Zhang
The prospect of assistive robots aiding in object organization has always been compelling. In an image-goal setting, the robot rearranges the current scene to match the single imag…
GraspGPT: Leveraging Semantic Knowledge from a Large Language Model for Task-Oriented Grasping
Chao Tang, Dehao Huang, Wenqi Ge +2
Task-oriented grasping (TOG) refers to the problem of predicting grasps on an object that enable subsequent manipulation tasks. To model the complex relationships between objects,…
Task-Oriented Grasp Prediction with Visual-Language Inputs
Chao Tang, Dehao Huang, Lingxiao Meng +2
To perform household tasks, assistive robots receive commands in the form of user language instructions for tool manipulation. The initial stage involves selecting the intended too…