7 citations · 20 across the 26 of their papers we have counts for
4 papers · 1 filter
AIC MLLM: Autonomous Interactive Correction MLLM for Robust Robotic Manipulation
Chuyan Xiong, Chengyu Shen, Xiaoqi Li +4
The ability to reflect on and correct failures is crucial for robotic systems to interact stably with real-life objects.Observing the generalization and reasoning capabilities of M…
RoboMamba: Efficient Vision-Language-Action Model for Robotic Reasoning and Manipulation
Jiaming Liu, Mengzhen Liu, Zhenyu Wang +7
A fundamental objective in robot manipulation is to enable models to comprehend visual scenes and execute actions. Although existing Vision-Language-Action (VLA) models for robots…
A Self-Correcting Vision-Language-Action Model for Fast and Slow System Manipulation
Chenxuan Li, Jiaming Liu, Guanqun Wang +8
Recently, some studies have integrated Multimodal Large Language Models into robotic manipulation, constructing vision-language-action models (VLAs) to interpret multimodal informa…
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
Xiangyang Zhu, Renrui Zhang, Bowei He +6
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning. Current 3D few-shot segmentation methods first pre-train models on 'see…