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cs.RO2025
See Once, Then Act: Vision-Language-Action Model with Task Learning from One-Shot Video Demonstrations
Guangyan Chen, Meiling Wang, Qi Shao +10
Developing robust and general-purpose manipulation policies represents a fundamental objective in robotics research. While Vision-Language-Action (VLA) models have demonstrated pro…
cs.RO2025
GLUE: Global-Local Unified Encoding for Imitation Learning via Key-Patch Tracking
Ye Chen, Zichen Zhou, Jianyu Dou +3
In recent years, visual representation learning has gained widespread attention in robotic imitation learning. However, in complex Out-of-Distribution(OOD) settings characterized b…
cs.RO2025
FMimic: Foundation Models are Fine-grained Action Learners from Human Videos
Guangyan Chen, Meiling Wang, Te Cui +8
Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in foundation models, particularly Vis…