8 papers
Training Generalist Navigation Policies via Action Self-Distillation
Bowen Feng, Keqin Wang, Yulong Yang +4
Language provides a natural interface for humans to specify and reason about goals, but training language-conditioned robotic policies and reasoning typically requires expensive ma…
Generating Physically Plausible Parachute Dynamics with Deep Generative Modeling
Yulong Yang, Clara O'Farrell, Christine Allen-Blanchette
Accurately modeling the dynamics of planetary parachute and entry vehicle systems is critical for Entry, Descent, and Landing events such as vehicle separation and sensor activatio…
Frequency-Separable Hamiltonian Neural Network for Multi-Timescale Dynamics
Yaojun Li, Yulong Yang, Christine Allen-Blanchette
While Hamiltonian mechanics provides a powerful inductive bias for neural networks modeling dynamical systems, Hamiltonian Neural Networks and their variants often fail to capture…
A Hypertoroidal Covering for Perfect Color Equivariance
Yulong Yang, Zhikun Xu, Yaojun Li +1
When the color distribution of input images changes at inference, the performance of conventional neural network architectures drops considerably. A few researchers have begun to i…
Discovering Phase Space Structure in Learned Hamiltonian Systems
Jiayin Liu, Yulong Yang, Vineet Bansal +1
Mechanics-inspired neural dynamical models improve interpretability and physical consistency, but they usually assume a known configuration space, fixed physical parameters, and de…
Resolving Oversmoothing with Opinion Dissensus
Keqin Wang, Yulong Yang, Ishan Saha +1
While graph neural networks (GNNs) have allowed researchers to successfully apply neural networks to non-Euclidean domains, deep GNNs often exhibit lower predictive performance tha…
Behavior-Inspired Neural Networks for Relational Inference
Yulong Yang, Bowen Feng, Keqin Wang +3
From pedestrians to Kuramoto oscillators, interactions between agents govern how dynamical systems evolve in space and time. Discovering how these agents relate to each other has t…
Learning Color Equivariant Representations
Yulong Yang, Felix O'Mahony, Christine Allen-Blanchette
In this paper, we introduce group convolutional neural networks (GCNNs) equivariant to color variation. GCNNs have been designed for a variety of geometric transformations from 2D…