14 citations · 32 across the 10 of their papers we have counts for
6 papers · 1 filter
Learning to Navigate in Mazes with Novel Layouts using Abstract Top-down Maps
Linfeng Zhao, Lawson L. S. Wong
Learning navigation capabilities in different environments has long been one of the major challenges in decision-making. In this work, we focus on zero-shot navigation ability usin…
Can Euclidean Symmetry be Leveraged in Reinforcement Learning and Planning?
Linfeng Zhao, Owen Howell, Jung Yeon Park +3
In robotic tasks, changes in reference frames typically do not influence the underlying physical properties of the system, which has been known as invariance of physical laws.These…
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…
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…
Learning Symmetric Embeddings for Equivariant World Models
Jung Yeon Park, Ondrej Biza, Linfeng Zhao +2
Incorporating symmetries can lead to highly data-efficient and generalizable models by defining equivalence classes of data samples related by transformations. However, characteriz…