1 citations · 1 across the 4 of their papers we have counts for
5 papers
SimpleTouch: Can Vision-Language-Action Models Master Contact-Rich Manipulation Without Tactile Policy Pretraining?
Chen Yang, Linzhe Shi, Changjie Wu +7
Tactile sensing provides essential contact information for robotic manipulation, yet incorporating it into pretrained vision-language-action (VLA) models remains challenging. A com…
Realtime-VLA V2: Learning to Run VLAs Fast, Smooth, and Accurate
Chen Yang, Yucheng Hu, Yunchao Ma +3
In deployment of the VLA models to real-world robotic tasks, execution speed matters. In previous work arXiv:2510.26742 we analyze how to make neural computation of VLAs on GPU fas…
SAGE:State-Aware Guided End-to-End Policy for Multi-Stage Sequential Tasks via Hidden Markov Decision Process
BinXu Wu, TengFei Zhang, Chen Yang +5
Multi-stage sequential (MSS) robotic manipulation tasks are prevalent and crucial in robotics. They often involve state ambiguity, where visually similar observations correspond to…
VLA-LPAF: Lightweight Perspective-Adaptive Fusion for Vision-Language-Action to Enable More Unconstrained Robotic Manipulation
Jinyue Bian, Zhaoxing Zhang, Zhengyu Liang +5
The Visual-Language-Action (VLA) models can follow text instructions according to visual observations of the surrounding environment. This ability to map multimodal inputs to actio…
Integrating Visual Foundation Models for Enhanced Robot Manipulation and Motion Planning: A Layered Approach
Chen Yang, Peng Zhou, Jiaming Qi
This paper presents a novel layered framework that integrates visual foundation models to improve robot manipulation tasks and motion planning. The framework consists of five layer…