11 papers
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…
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…
CoA-VLA: Improving Vision-Language-Action Models via Visual-Textual Chain-of-Affordance
Jinming Li, Yichen Zhu, Zhibin Tang +8
Robot foundation models, particularly Vision-Language-Action (VLA) models, have garnered significant attention for their ability to enhance robot policy learning, greatly improving…
Diffusion-VLA: Generalizable and Interpretable Robot Foundation Model via Self-Generated Reasoning
Junjie Wen, Minjie Zhu, Yichen Zhu +8
In this paper, we present DiffusionVLA, a novel framework that seamlessly combines the autoregression model with the diffusion model for learning visuomotor policy. Central to our…
TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation
Junjie Wen, Yichen Zhu, Jinming Li +9
Vision-Language-Action (VLA) models have shown remarkable potential in visuomotor control and instruction comprehension through end-to-end learning processes. However, current VLA…
ACL-QL: Adaptive Conservative Level in Q-Learning for Offline Reinforcement Learning
Kun Wu, Yinuo Zhao, Zhiyuan Xu +5
Offline Reinforcement Learning (RL), which operates solely on static datasets without further interactions with the environment, provides an appealing alternative to learning a saf…