Xinmin Fang — accepted AI-conference papers

2025

DexPour: Effective and Efficient High-DoF Robotic Hand Liquid Pouring via Hierarchical Reward with Approximated Proxy Abstraction

Xinmin Fang, Lingfeng Tao, Zhengxiong Li

IROS 2025

Pouring fluids is a routine task for humans but challenging for high-DoF robots, particularly given fluid simulation’s computational demands while training policies. In this paper, we propose DexPour, a novel reinforcement learning method with hierarchical rewards and Approximated Proxy Abstraction

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2025

VRobotix: A Scalable and Cost-Effective Virtual-Reality-Based Robotic Manipulation Dataset Generation Framework

Xinmin Fang, Zheshuo Li, Lingfeng Tao, Zhengxiong Li

IROS 2025

Large-scale, diverse datasets are essential for training robust learning-based robotic manipulation models; however, their acquisition typically requires controlled environments and specialized hardware in research laboratories. This paper presents VRobotix, a virtual reality (VR)-based framework th

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