Haoxiang Qin — accepted AI-conference papers

2026

Force Policy: Learning Hybrid Force-Position Control Policy under Interaction Frame for Contact-Rich Manipulation

Hongjie Fang, Shirun Tang, Mingyu Mei, Haoxiang Qin, Zihao He, Jingjing Chen +6

RSS 2026poster

Contact-rich manipulation demands human-like integration of perception and force feedback: vision should guide task progress, while high-frequency interaction control must stabilize contact under uncertainty. Existing learning-based policies often entangle these roles in a monolithic network, tradin…

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2026

Progressive Adversarial Multi-View Alignment for Unsupervised Embedded Feature Selection with Linear Complexity

Shixuan Zhou, Yi Xiang, Haoxiang Qin, Haining Wang, Yukuan Ma, Han Huang

IJCAI 2026

Standard unsupervised multi-view feature selection (UMFS) methods for large datasets exhibit limitations in modeling the competition between cross-view alignment and intra-view diversity, resulting in suboptimal solutions and expensive computational costs. This challenge is exacerbated by the divers

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