5 citations · 9 across the 2 of their papers we have counts for
5 papers · 1 filter
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…
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.…