1 citations · 1 across the 9 of their papers we have counts for
10 papers
SAFE-Pruner: Semantic Attention-Guided Future-Aware Token Pruning for Efficient Vision-Language-Action Manipulation
Shilin Ma, Chubin Zhang, Changyuan Wang +6
Real-time inference of vision-language-action (VLA) models is essential for robotic control. While visual token pruning has shown strong potential for accelerating inference, most…
GigaWorld-Policy: An Efficient Action-Centered World--Action Model
Angen Ye, Boyuan Wang, Chaojun Ni +21
World-Action Models (WAM) initialized from pre-trained video generation backbones have demonstrated remarkable potential for robot policy learning. However, existing approaches fac…
-StepNFT: Wider Space Needs Finer Steps in Online RL for Flow-based VLAs
Siting Wang, Xiaofeng Wang, Zheng Zhu +7
Flow-based vision-language-action (VLA) models have emerged as a powerful paradigm for embodied control, but multi-step denoising makes policy-gradient online reinforcement learnin…
TacMamba: A Tactile History Compression Adapter Bridging Fast Reflexes and Slow VLA Reasoning
Zhenan Wang, Yanzhe Wang, Meixuan Ren +8
In visually ambiguous manipulation such as detecting button click tactile feedback is often the sole source of ground truth. However, fusing tactile data poses a significant challe…
World Guidance: World Modeling in Condition Space for Action Generation
Yue Su, Sijin Chen, Haixin Shi +7
Leveraging future observation modeling to facilitate action generation presents a promising avenue for enhancing the capabilities of Vision-Language-Action (VLA) models. However, e…
SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead
Chaojun Ni, Cheng Chen, Xiaofeng Wang +12
Vision-Language-Action (VLA) models built on pretrained Vision-Language Models (VLMs) show strong potential but are limited in practicality due to their large parameter counts. To…
GigaWorld-0: World Models as Data Engine to Empower Embodied AI
GigaWorld Team, Angen Ye, Boyuan Wang +22
World models are emerging as a foundational paradigm for scalable, data-efficient embodied AI. In this work, we present GigaWorld-0, a unified world model framework designed explic…
VLA-R1: Enhancing Reasoning in Vision-Language-Action Models
Angen Ye, Zeyu Zhang, Boyuan Wang +3
Vision-Language-Action (VLA) models aim to unify perception, language understanding, and action generation, offering strong cross-task and cross-scene generalization with broad imp…
GigaBrain-0: A World Model-Powered Vision-Language-Action Model
GigaBrain Team, Angen Ye, Boyuan Wang +24
Training Vision-Language-Action (VLA) models for generalist robots typically requires large-scale real-world robot data, which is expensive and time-consuming to collect. The ineff…
From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors
Zhengshen Zhang, Hao Li, Yalun Dai +10
Existing vision-language-action (VLA) models act in 3D real-world but are typically built on 2D encoders, leaving a spatial reasoning gap that limits generalization and adaptabilit…