5 citations · 9 across the 2 of their papers we have counts for
6 papers
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…
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.…