43 citations · 81 across the 10 of their papers we have counts for
10 papers
OmniPose6D: Towards Short-Term Object Pose Tracking in Dynamic Scenes from Monocular RGB
Yunzhi Lin, Yipu Zhao, Fu-Jen Chu +6
To address the challenge of short-term object pose tracking in dynamic environments with monocular RGB input, we introduce a large-scale synthetic dataset OmniPose6D, crafted to mi…
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…
Using Synthetic Data and Deep Networks to Recognize Primitive Shapes for Object Grasping
Yunzhi Lin, Chao Tang, Fu-Jen Chu +1
A segmentation-based architecture is proposed to decompose objects into multiple primitive shapes from monocular depth input for robotic manipulation. The backbone deep network is…
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…
Learning to Navigate: Exploiting Deep Networks to Inform Sample-Based Planning During Vision-Based Navigation
Justin S. Smith, Jin-Ha Hwang, Fu-Jen Chu +1
Recent applications of deep learning to navigation have generated end-to-end navigation solutions whereby visual sensor input is mapped to control signals or to motion primitives.…
When Crowdsourcing Meets Mobile Sensing: A Social Network Perspective
Pin-Yu Chen, Shin-Ming Cheng, Pai-Shun Ting +2
Mobile sensing is an emerging technology that utilizes agent-participatory data for decision making or state estimation, including multimedia applications. This article investigate…
Supervised Collective Classification for Crowdsourcing
Pin-Yu Chen, Chia-Wei Lien, Fu-Jen Chu +2
Crowdsourcing utilizes the wisdom of crowds for collective classification via information (e.g., labels of an item) provided by labelers. Current crowdsourcing algorithms are mainl…