Yingke Wang — accepted AI-conference papers

2026

IMPASTO: Integrating Model-Based Planning with Learned Dynamics Models for Robotic Oil Painting Reproduction

Yingke Wang, Hao Li, Yifeng Zhu, Hong-Xing Yu, Ken Goldberg, Li Fei-Fei +3

ICRA 2026poster

Robotic reproduction of oil paintings using soft brushes and pigments requires force-sensitive control of deformable tools, prediction of brushstroke effects, and multi-step stroke planning, often without human step-by-step demonstrations or faithful simulators. Given only a sequence of target oil p…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

UAD: Unsupervised Affordance Distillation for Generalization in Robotic Manipulation

Yihe Tang, Wenlong Huang, Yingke Wang, Chengshu Li, Roy Yuan, Ruohan Zhang +2

ICRA 2025

Understanding fine-grained object affordances is imperative for robots to manipulate objects in unstructured environments given open-ended task instructions. However, existing methods of visual affordance predictions often rely on manually annotated data or conditions only on a predefined set of tas

Cited by 23 Source ↗code ↗Scholar ↗