13 citations · 42 across the 25 of their papers we have counts for
5 papers · 1 filter
Let Me Show You: Learning by Retrieving from Egocentric Video for Robotic Manipulation
Yichen Zhu, Feifei Feng
Robots operating in complex and uncertain environments face considerable challenges. Advanced robotic systems often rely on extensive datasets to learn manipulation tasks. In contr…
PointVLA: Injecting the 3D World into Vision-Language-Action Models
Chengmeng Li, Junjie Wen, Yan Peng +3
Vision-Language-Action (VLA) models excel at robotic tasks by leveraging large-scale 2D vision-language pretraining, but their reliance on RGB images limits spatial reasoning criti…
ObjectVLA: End-to-End Open-World Object Manipulation Without Demonstration
Minjie Zhu, Yichen Zhu, Jinming Li +6
Imitation learning has proven to be highly effective in teaching robots dexterous manipulation skills. However, it typically relies on large amounts of human demonstration data, wh…
ChatVLA: Unified Multimodal Understanding and Robot Control with Vision-Language-Action Model
Zhongyi Zhou, Yichen Zhu, Minjie Zhu +8
Humans possess a unified cognitive ability to perceive, comprehend, and interact with the physical world. Why can't large language models replicate this holistic understanding? Thr…
DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control
Junjie Wen, Yichen Zhu, Jinming Li +3
Enabling robots to perform diverse tasks across varied environments is a central challenge in robot learning. While vision-language-action (VLA) models have shown promise for gener…