most citedMeta-learning Structure-Preserving Dynamics

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.LG2026

Extending Fourier Neural Operators for Modeling Parameterized and Coupled PDEs

Cheng Jing, Uvini Balasuriya Mudiyanselage, Abhishek Verma +3

Parameterized and coupled partial differential equations (PDEs) are central to modeling phenomena in science and engineering, yet neural operator methods that address both aspects…

cs.LG20261 cited

Meta-learning Structure-Preserving Dynamics

Cheng Jing, Uvini Balasuriya Mudiyanselage, Woojin Cho +3

Structure-preserving approaches to dynamics discovery have demonstrated great potential for modeling physical systems due to their use of strong inductive biases, which enforce key…

cs.RO2026

LSRE: Latent Semantic Rule Encoding for Real-Time Semantic Risk Detection in Autonomous Driving

Qian Cheng, Weitao Zhou, Cheng Jing +5

Real-world autonomous driving must adhere to complex human social rules that extend beyond legally codified traffic regulations. Many of these semantic constraints, such as yieldin…

cs.RO2025

Are All Data Necessary? Efficient Data Pruning for Large-scale Autonomous Driving Dataset via Trajectory Entropy Maximization

Zhaoyang Liu, Weitao Zhou, Junze Wen +4

Collecting large-scale naturalistic driving data is essential for training robust autonomous driving planners. However, real-world datasets often contain a substantial amount of re…

cs.RO2025

AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems

AgiBot-World-Contributors, Qingwen Bu, Jisong Cai +49

We explore how scalable robot data can address real-world challenges for generalized robotic manipulation. Introducing AgiBot World, a large-scale platform comprising over 1 millio…