activity
20242026
most citedDynamic Perturbation-Adaptive Adversarial Training on Medical Image Classification

1 citations · 1 across the 17 of their papers we have counts for

collaborators
Showing cs.ROShow all

6 papers · 1 filter

cs.RO2026

Acting While Understanding: Asynchronous Semantic-Action Decoupling for Real-Time Vision-Language-Action Models

Shenhao Yan, Ge Wang, Qi Liu +7

Vision-Language-Action models (VLAs) have demonstrated strong task understanding and generalization in robotic manipulation, yet the high computational cost of full-model inference…

cs.RO2026

PM-Nav: Priori-Map Guided Embodied Navigation in Functional Buildings

Jiang Gao, Xiangyu Dong, Haozhou Li +3

Existing language-driven embodied navigation paradigms face challenges in functional buildings (FBs) with highly similar features, as they lack the ability to effectively utilize p…

cs.RO2026

Mean-Flow based One-Step Vision-Language-Action

Yang Chen, Xiaoguang Ma, Bin Zhao

Recent advances in FlowMatching-based Vision-Language-Action (VLA) frameworks have demonstrated remarkable advantages in generating high-frequency action chunks, particularly for h…

cs.RO2026

PhyGile: Physics-Prefix Guided Motion Generation for Agile General Humanoid Motion Tracking

Jiacheng Bao, Haoran Yang, Yucheng Xin +7

Humanoid robots are expected to execute agile and expressive whole-body motions in real-world settings. Existing text-to-motion generation models are predominantly trained on captu…

cs.RO2024

Subconscious Robotic Imitation Learning

Jun Xie, Zhicheng Wang, Jianwei Tan +2

Although robotic imitation learning (RIL) is promising for embodied intelligent robots, existing RIL approaches rely on computationally intensive multi-model trajectory predictions…

cs.RO2024

Constrained Behavior Cloning for Robotic Learning

Wensheng Liang, Jun Xie, Zhicheng Wang +2

Behavior cloning (BC) is a popular supervised imitation learning method in the societies of robotics, autonomous driving, etc., wherein complex skills can be learned by direct imit…