237 citations · 1.1k across the 38 of their papers we have counts for
12 papers · 1 filter
Temporal and Object Quantification Networks
Jiayuan Mao, Zhezheng Luo, Chuang Gan +4
We present Temporal and Object Quantification Networks (TOQ-Nets), a new class of neuro-symbolic networks with a structural bias that enables them to learn to recognize complex rel…
Language-Mediated, Object-Centric Representation Learning
Ruocheng Wang, Jiayuan Mao, Samuel J. Gershman +1
We present Language-mediated, Object-centric Representation Learning (LORL), a paradigm for learning disentangled, object-centric scene representations from vision and language. LO…
Augmenting Policy Learning with Routines Discovered from a Single Demonstration
Zelin Zhao, Chuang Gan, Jiajun Wu +2
Humans can abstract prior knowledge from very little data and use it to boost skill learning. In this paper, we propose routine-augmented policy learning (RAPL), which discovers ro…
Visual Grounding of Learned Physical Models
Yunzhu Li, Toru Lin, Kexin Yi +5
Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. The abilities to perform physical…
Entity Abstraction in Visual Model-Based Reinforcement Learning
Rishi Veerapaneni, John D. Co-Reyes, Michael Chang +5
This paper tests the hypothesis that modeling a scene in terms of entities and their local interactions, as opposed to modeling the scene globally, provides a significant benefit i…
Learning Compositional Koopman Operators for Model-Based Control
Yunzhu Li, Hao He, Jiajun Wu +2
Finding an embedding space for a linear approximation of a nonlinear dynamical system enables efficient system identification and control synthesis. The Koopman operator theory lay…