4 citations · 7 across the 3 of their papers we have counts for
5 papers · 1 filter
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…
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…