9 papers
DriftingVLA: Native One-Step Vision-Language-Action Generation via Per-Dimension Temporal Drifting
Yuxuan Gao, Shiqi Zhang, Yedong Shen +6
Conventional flow-based vision-language-action (VLA) models support expressive continuous action generation but rely on multi-step refinement to produce each action chunk, increasi…
CAC-VLA: Context-Gated Action Conditioning for Vision-Language-Action Models
Yifu Xiong, Wenhao Yu, Jiaxuan Lin +5
Vision-Language-Action (VLA) models have become a promising paradigm for generalist robot manipulation, where visual-language representations are used to condition continuous actio…
DynaHMRC: Decentralized Heterogeneous Multi-Robot Collaboration for Dynamic Tasks with Large Language Models
Wenhao Yu, Yu'ang Xie, Yifan Duan +5
Large language models (LLMs) provide robots with richer task understanding and adaptability, making them promising for coordinating heterogeneous multi-robot systems in long-horizo…
GA-GS: Generation-Assisted Gaussian Splatting for Static Scene Reconstruction
Yedong Shen, Shiqi Zhang, Sha Zhang +6
Reconstructing static 3D scene from monocular video with dynamic objects is important for numerous applications such as virtual reality and autonomous driving. Current approaches t…
Drift-Based Policy Optimization: Native One-Step Policy Learning for Online Robot Control
Yuxuan Gao, Yedong Shen, Shiqi Zhang +6
Diffusion policies effectively model multimodal action distributions for robotic manipulation, but their iterative denoising requires tens to hundreds of network function evaluatio…
STDArm: Transferring Visuomotor Policies From Static Data Training to Dynamic Robot Manipulation
Yifan Duan, Heng Li, Yilong Wu +5
Recent advances in mobile robotic platforms like quadruped robots and drones have spurred a demand for deploying visuomotor policies in increasingly dynamic environments. However,…
MHRC: Closed-loop Decentralized Multi-Heterogeneous Robot Collaboration with Large Language Models
Wenhao Yu, Jie Peng, Yueliang Ying +3
The integration of large language models (LLMs) with robotics has significantly advanced robots' abilities in perception, cognition, and task planning. The use of natural language…
LDP: A Local Diffusion Planner for Efficient Robot Navigation and Collision Avoidance
Wenhao Yu, Jie Peng, Huanyu Yang +4
The conditional diffusion model has been demonstrated as an efficient tool for learning robot policies, owing to its advancement to accurately model the conditional distribution of…
PathRL: An End-to-End Path Generation Method for Collision Avoidance via Deep Reinforcement Learning
Wenhao Yu, Jie Peng, Quecheng Qiu +3
Robot navigation using deep reinforcement learning (DRL) has shown great potential in improving the performance of mobile robots. Nevertheless, most existing DRL-based navigation m…