Co Yong

Co Yong

PhD student in National Taiwan University.

Publications

You can also find my articles on my Google Scholar profile.

Journal Articles

Restoring Noisy Demonstration for Imitation Learning With Diffusion Models

Published in IEEE Transactions on Neural Networks and Learning Systems, 2026

Most imitation learning methods assume perfect expert demonstrations, yet real data is often noisy. We propose a filter-and-restore framework that isolates clean samples and uses conditional diffusion models to recover noisy demonstrations. Experiments on robot arm manipulation, dexterous manipulation, and locomotion show consistent improvements over existing methods, with ablations confirming robustness to diverse noise types and levels.

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Workshop Papers

Less Tuning, Better Planning: Simplifying Offline Model-Based Planning

Published in ICML 2026 workshop on Decision-Making from Offline Datasets to Online Adaptation: Black-Box Optimization to Reinforcement Learning, 2026

Offline model-based planning can adapt policies at test time, but performance depends on a planning horizon and action proposer that are often tuned online. We propose SHARP (Soft Horizon AggRegation for Planning), which weights multi-horizon returns by ensemble dynamics uncertainty to avoid fixed-horizon tuning. SHARP-BC pairs this with a simple behavior-cloning action proposer, matching or beating baselines with less hyperparameter search.

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