7 citations · 12 across the 4 of their papers we have counts for
8 papers
Navigate Complex Physical Worlds via Geometrically Constrained LLM
Yongqiang Huang, Wentao Ye, Liyao Li +1
This study investigates the potential of Large Language Models (LLMs) for reconstructing and constructing the physical world solely based on textual knowledge. It explores the impa…
Robot Gaining Accurate Pouring Skills through Self-Supervised Learning and Generalization
Yongqiang Huang, Juan Wilches, Yu Sun
Pouring is one of the most commonly executed tasks in humans' daily lives, whose accuracy is affected by multiple factors, including the type of material to be poured and the geome…
Manipulation Motion Taxonomy and Coding for Robots
David Paulius, Yongqiang Huang, Jason Meloncon +1
This paper introduces a taxonomy of manipulations as seen especially in cooking for 1) grouping manipulations from the robotics point of view, 2) consolidating aliases and removing…
Accurate Robotic Pouring for Serving Drinks
Yongqiang Huang, Yu Sun
Pouring is the second most frequently executed motion in cooking scenarios. In this work, we present our system of accurate pouring that generates the angular velocities of the sou…
Functional Object-Oriented Network for Manipulation Learning
David Paulius, Yongqiang Huang, Roger Milton +3
This paper presents a novel structured knowledge representation called the functional object-oriented network (FOON) to model the connectivity of the functional-related objects and…
A Dataset of Daily Interactive Manipulation
Yongqiang Huang, Yu Sun
Robots that succeed in factories stumble to complete the simplest daily task humans take for granted, for the change of environment makes the task exceedingly difficult. Aiming to…