14 citations · 32 across the 12 of their papers we have counts for
12 papers
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…
KGNv2: Separating Scale and Pose Prediction for Keypoint-based 6-DoF Grasp Synthesis on RGB-D input
Yiye Chen, Ruinian Xu, Yunzhi Lin +2
We propose a new 6-DoF grasp pose synthesis approach from 2D/2.5D input based on keypoints. Keypoint-based grasp detector from image input has demonstrated promising results in the…
Zero-Shot Object Searching Using Large-scale Object Relationship Prior
Hongyi Chen, Ruinian Xu, Shuo Cheng +2
Home-assistant robots have been a long-standing research topic, and one of the biggest challenges is searching for required objects in housing environments. Previous object-goal na…
WDiscOOD: Out-of-Distribution Detection via Whitened Linear Discriminant Analysis
Yiye Chen, Yunzhi Lin, Ruinian Xu +1
Deep neural networks are susceptible to generating overconfident yet erroneous predictions when presented with data beyond known concepts. This challenge underscores the importance…
Keypoint-GraspNet: Keypoint-based 6-DoF Grasp Generation from the Monocular RGB-D input
Yiye Chen, Yunzhi Lin, Ruinian Xu +1
Great success has been achieved in the 6-DoF grasp learning from the point cloud input, yet the computational cost due to the point set orderlessness remains a concern. Alternative…
SGL: Symbolic Goal Learning in a Hybrid, Modular Framework for Human Instruction Following
Ruinian Xu, Hongyi Chen, Yunzhi Lin +1
This paper investigates robot manipulation based on human instruction with ambiguous requests. The intent is to compensate for imperfect natural language via visual observations. E…
Primitive Shape Recognition for Object Grasping
Yunzhi Lin, Chao Tang, Fu-Jen Chu +2
Shape informs how an object should be grasped, both in terms of where and how. As such, this paper describes a segmentation-based architecture for decomposing objects sensed with a…
GKNet: grasp keypoint network for grasp candidates detection
Ruinian Xu, Fu-Jen Chu, Patricio A. Vela
Contemporary grasp detection approaches employ deep learning to achieve robustness to sensor and object model uncertainty. The two dominant approaches design either grasp-quality s…
A Joint Network for Grasp Detection Conditioned on Natural Language Commands
Yiye Chen, Ruinian Xu, Yunzhi Lin +1
We consider the task of grasping a target object based on a natural language command query. Previous work primarily focused on localizing the object given the query, which requires…
Recognizing Object Affordances to Support Scene Reasoning for Manipulation Tasks
Fu-Jen Chu, Ruinian Xu, Chao Tang +1
Affordance information about a scene provides important clues as to what actions may be executed in pursuit of meeting a specified goal state. Thus, integrating affordance-based re…
Real-world Multi-object, Multi-grasp Detection
Fu-Jen Chu, Ruinian Xu, Patricio A. Vela
A deep learning architecture is proposed to predict graspable locations for robotic manipulation. It considers situations where no, one, or multiple object(s) are seen. By defining…
The Helping Hand: An Assistive Manipulation Framework Using Augmented Reality and a Tongue-Drive Interfaces
Fu-Jen Chu, Ruinian Xu, Zhenxuan Zhang +2
A human-in-the-loop system is proposed to enable collaborative manipulation tasks for person with physical disabilities. Studies show that the cognitive burden of subject reduces w…