24 citations · 75 across the 22 of their papers we have counts for
7 papers · 1 filter
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Dian Wang, Jung Yeon Park, Neel Sortur +3
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network archit…
Robust Imitation of a Few Demonstrations with a Backwards Model
Jung Yeon Park, Lawson L. S. Wong
Behavior cloning of expert demonstrations can speed up learning optimal policies in a more sample-efficient way over reinforcement learning. However, the policy cannot extrapolate…
Scaling up and Stabilizing Differentiable Planning with Implicit Differentiation
Linfeng Zhao, Huazhe Xu, Lawson L. S. Wong
Differentiable planning promises end-to-end differentiability and adaptivity. However, an issue prevents it from scaling up to larger-scale problems: they need to differentiate thr…
Integrating Symmetry into Differentiable Planning with Steerable Convolutions
Linfeng Zhao, Xupeng Zhu, Lingzhi Kong +2
We study how group symmetry helps improve data efficiency and generalization for end-to-end differentiable planning algorithms when symmetry appears in decision-making tasks. Motiv…
Binding Actions to Objects in World Models
Ondrej Biza, Robert Platt, Jan-Willem van de Meent +2
We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms. We propose two attention mechanisms for binding actions to obj…
Toward Compositional Generalization in Object-Oriented World Modeling
Linfeng Zhao, Lingzhi Kong, Robin Walters +1
Compositional generalization is a critical ability in learning and decision-making. We focus on the setting of reinforcement learning in object-oriented environments to study compo…
Factored World Models for Zero-Shot Generalization in Robotic Manipulation
Ondrej Biza, Thomas Kipf, David Klee +3
World models for environments with many objects face a combinatorial explosion of states: as the number of objects increases, the number of possible arrangements grows exponentiall…