Kuan-Hsun Tu — accepted AI-conference papers

2026

COT-FM: Cluster-wise Optimal Transport Flow Matching

Chiensheng Chiang, Kuan-Hsun Tu, Jia-Wei Liao, Cheng-Fu Chou, Tsung-Wei Ke

CVPR 2026

We introduce COT-FM, a general framework that reshapes the probability path in Flow Matching (FM) to achieve faster and more reliable generation. FM models often produce curved trajectories due to random or batch-wise couplings, which increase discretization error and reduce sample quality. COT-FM f

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2026

RobotArena $\infty$: Unlimited Robot Benchmarking via Real-to-Sim Translation

Yash Jangir, Yidi Zhang, Kashu Yamazaki, Chenyu Zhang, Kuan-Hsun Tu, Tsung-Wei Ke +3

ICLR 2026poster

The pursuit of robot generalists—instructable agents capable of performing diverse tasks across diverse environments—demands rigorous and scalable evaluation. Yet real-world testing of robot policies remains fundamentally constrained: it is labor-intensive, slow, unsafe at scale, and difficult to re…

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