activity
20202025
most citedDiffusion Models for Reinforcement Learning: A Survey

12 citations · 18 across the 5 of their papers we have counts for

collaborators

6 papers

cs.RO2025★ 1 cited

GR-RL: Going Dexterous and Precise for Long-Horizon Robotic Manipulation

Yunfei Li, Xiao Ma, Jiafeng Xu +18

We present GR-RL, a robotic learning framework that turns a generalist vision-language-action (VLA) policy into a highly capable specialist for long-horizon dexterous manipulation.…

cs.MA2025

SocialDriveGen: Generating Diverse Traffic Scenarios with Controllable Social Interactions

Jiaguo Tian, Zhengbang Zhu, Shenyu Zhang +6

The generation of realistic and diverse traffic scenarios in simulation is essential for developing and evaluating autonomous driving systems. However, most simulation frameworks r…

cs.AI2025

DriveGen: Towards Infinite Diverse Traffic Scenarios with Large Models

Shenyu Zhang, Jiaguo Tian, Zhengbang Zhu +3

Microscopic traffic simulation has become an important tool for autonomous driving training and testing. Although recent data-driven approaches advance realistic behavior generatio…

cs.LG2023★ 12 cited

Diffusion Models for Reinforcement Learning: A Survey

Zhengbang Zhu, Hanye Zhao, Haoran He +5

Diffusion models surpass previous generative models in sample quality and training stability. Recent works have shown the advantages of diffusion models in improving reinforcement…

cs.AI2022★ 4 cited

RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow

Zhengbang Zhu, Shenyu Zhang, Yuzheng Zhuang +11

High-quality traffic flow generation is the core module in building simulators for autonomous driving. However, the majority of available simulators are incapable of replicating tr…

cs.CV2020★ 1 cited

Rb-PaStaNet: A Few-Shot Human-Object Interaction Detection Based on Rules and Part States

Shenyu Zhang, Zichen Zhu, Qingquan Bao

Existing Human-Object Interaction (HOI) Detection approaches have achieved great progress on nonrare classes while rare HOI classes are still not well-detected. In this paper, we i…