1 citations · 1 across the 7 of their papers we have counts for
8 papers
ChunkTrust: Adapting Execution Horizons for Robot Policies with Action-Expert Evidence
Fanding Huang, Jingyan Jiang, Shifeng Bao +13
Robot foundation policies predict action chunks, but how many actions to execute before replanning depends on the current task phase. We introduce ChunkTrust, which treats the exec…
GeniWorld: A Generalizable Interactive World Model for Robotic Manipulation via Visual Actions
Chenghao Gu, Hanyang Yu, Jingbo Zhang +7
Generalist robot policies exhibit strong capabilities, but their robustness in complex and unseen environments remains limited. Scaling robot learning and evaluation in diverse rea…
MaskWAM: Unifying Mask Prompting and Prediction for World-Action Models
Hanyang Yu, Haitao Lin, Jingbo Zhang +4
World Action Models (WAMs) present a promising paradigm for robotic control via video prediction. However, current WAMs suffer from fundamental spatial bottlenecks: standard text i…
HDSL: A Hierarchical Domain-Specific Language for Structured 3D Indoor Scene Generation and Localized Editing with LLM Agents
Letian Li, Chao Shen, Shuzhao Xie +6
Text-driven indoor scene generation and editing require an intermediate representation that language models can both produce and revise. Existing LLM-based systems often rely on sc…
IGen: Scalable Data Generation for Robot Learning from Open-World Images
Chenghao Gu, Haolan Kang, Junchao Lin +10
The rise of generalist robotic policies has created an exponential demand for large-scale training data. However, on-robot data collection is labor-intensive and often limited to s…
VisuCraft: Enhancing Large Vision-Language Models for Complex Visual-Guided Creative Content Generation via Structured Information Extraction
Rongxin Jiang, Robert Long, Chenghao Gu +1
This paper introduces VisuCraft, a novel framework designed to significantly enhance the capabilities of Large Vision-Language Models (LVLMs) in complex visual-guided creative cont…
DragScene: Interactive 3D Scene Editing with Single-view Drag Instructions
Chenghao Gu, Zhenzhe Li, Zhengqi Zhang +3
3D editing has shown remarkable capability in editing scenes based on various instructions. However, existing methods struggle with achieving intuitive, localized editing, such as…
Tuning-Free Visual Customization via View Iterative Self-Attention Control
Xiaojie Li, Chenghao Gu, Shuzhao Xie +3
Fine-Tuning Diffusion Models enable a wide range of personalized generation and editing applications on diverse visual modalities. While Low-Rank Adaptation (LoRA) accelerates the…