activity
20212026
most citedBack to Newton's Laws: Learning Vision-based Agile Flight via Differentiable Physics

43 citations · 60 across the 27 of their papers we have counts for

collaborators

28 papers

cs.RO2026

CrashDiffuser: VLM-Guided Collision Intent Reasoning for Fine-Grained Safety-Critical Traffic Scenario Generation

Shucheng Zhang, Yuang Zhang, Bingzhang Wang +3

Generating safety-critical scenarios is essential for evaluating autonomous driving systems. However, existing generators primarily focus on inducing collisions and offer limited c…

cs.RO2026

Asymmetric physics enables efficient learning in quadrupedal robot swarms

Yuang Zhang, Yunlong Song, Zhihao He +7

Animal collectives navigate cluttered environments through local coordination, yet robot swarms still struggle to reproduce this capability in the physical world. End-to-end learni…

cs.LG2026

Prototype-Guided Classification Sub-Task Decoupling Framework: Enhancing Generalization and Interpretability for Multivariate Time Series

Xianhao Song, Yuang Zhang, Yuqi She +2

Time Series Classification (TSC) is a long-standing research problem that has gained increasing attention in recent years with the rapid growth of large-scale temporal data. Despit…

cs.RO2026

Agent-Centric Observation Adaptation for Robust Visual Control under Dynamic Perturbations

Zhengru Fang, Yu Guo, Fei Liu +5

Real-world visual systems face time-varying perturbations, including weather, sensor noise, compression artifacts, and background distractions. Existing image restoration methods a…

cs.CV2026

CT-1: Vision-Language-Camera Models Transfer Spatial Reasoning Knowledge to Camera-Controllable Video Generation

Haoyu Zhao, Zihao Zhang, Jiaxi Gu +10

Camera-controllable video generation aims to synthesize videos with flexible and physically plausible camera movements. However, existing methods either provide imprecise camera co…

cs.CV2026

TAU-R1: Visual Language Model for Traffic Anomaly Understanding

Yuqiang Lin, Kehua Chen, Sam Lockyer +12

Traffic Anomaly Understanding (TAU) is important for traffic safety in Intelligent Transportation Systems. Recent vision-language models (VLMs) have shown strong capabilities in vi…