1 citations · 1 across the 8 of their papers we have counts for
8 papers
HOST:Robots Acquire Manipulation Skills in Seconds from a Single Human Video
Guangyan Chen, Meiling Wang, Te Cui +9
The ability to acquire skills rapidly and effortlessly while retaining those already mastered is essential for robots. However, current methods still rely on a cumbersome training-…
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…
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…
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…
TASeg: Text-aware RGB-T Semantic Segmentation based on Fine-tuning Vision Foundation Models
Meng Yu, Te Cui, Qitong Chu +3
Reliable semantic segmentation of open environments is essential for intelligent systems, yet significant problems remain: 1) Existing RGB-T semantic segmentation models mainly rel…
VLMimic: Vision Language Models are Visual Imitation Learner for Fine-grained Actions
Guanyan Chen, Meiling Wang, Te Cui +9
Visual imitation learning (VIL) provides an efficient and intuitive strategy for robotic systems to acquire novel skills. Recent advancements in Vision Language Models (VLMs) have…
VIFNet: An End-to-end Visible-Infrared Fusion Network for Image Dehazing
Meng Yu, Te Cui, Haoyang Lu +1
Image dehazing poses significant challenges in environmental perception. Recent research mainly focus on deep learning-based methods with single modality, while they may result in…
Human Demonstrations are Generalizable Knowledge for Robots
Te Cui, Tianxing Zhou, Zicai Peng +6
Learning from human demonstrations is an emerging trend for designing intelligent robotic systems. However, previous methods typically regard videos as instructions, simply dividin…