6 papers
Learning Linear Attention in Polynomial Time
Morris Yau, Ekin Akyürek, Jiayuan Mao +3
Previous research has explored the computational expressivity of Transformer models in simulating Boolean circuits or Turing machines. However, the learnability of these simulators…
"Set It Up": Functional Object Arrangement with Compositional Generative Models (Journal Version)
Yiqing Xu, Jiayuan Mao, Linfeng Li +4
Functional object arrangement (FORM) is the task of arranging objects to fulfill a function, e.g., "set up a dining table for two". One key challenge here is that the instructions…
Neuro-Symbolic Concepts
Jiayuan Mao, Joshua B. Tenenbaum, Jiajun Wu
This article presents a concept-centric paradigm for building agents that can learn continually and reason flexibly. The concept-centric agent utilizes a vocabulary of neuro-symbol…
"Set It Up!": Functional Object Arrangement with Compositional Generative Models
Yiqing Xu, Jiayuan Mao, Yilun Du +3
This paper studies the challenge of developing robots capable of understanding under-specified instructions for creating functional object arrangements, such as "set up a dining ta…
One-Shot Manipulation Strategy Learning by Making Contact Analogies
Yuyao Liu, Jiayuan Mao, Joshua Tenenbaum +2
We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive general…
Keypoint Abstraction using Large Models for Object-Relative Imitation Learning
Xiaolin Fang, Bo-Ruei Huang, Jiayuan Mao +4
Generalization to novel object configurations and instances across diverse tasks and environments is a critical challenge in robotics. Keypoint-based representations have been prov…