7 papers
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…
ImageWAM: Do World Action Models Really Need Video Generation, or Just Image Editing?
Yuyang Zhang, Wenyao Zhang, Zekun Qi +7
World Action Models (WAMs) commonly rely on video generation to bridge visual world modeling and robot control. However, video-based WAMs face three coupled limitations: dense mult…
Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack
He Zhang, Lingzhu Xiang, Haitao Lin +23
In this report, we present Hy-Embodied-0.5-VLA, abbreviated as HyVLA-0.5, an end-to-end system that spans the full robot learning stack: data collection, model design, continued pr…
HY-Embodied-0.5: Embodied Foundation Models for Real-World Agents
Tencent Robotics X, HY Vision Team, : +20
We introduce HY-Embodied-0.5, a family of foundation models specifically designed for real-world embodied agents. To bridge the gap between general Vision-Language Models (VLMs) an…
Spacetime-GR: A Spacetime-Aware Generative Model for Large Scale Online POI Recommendation
Haitao Lin, Zhen Yang, Jiawei Xue +5
Building upon the strong sequence modeling capability, Generative Recommendation (GR) has gradually assumed a dominant position in the application of recommendation tasks (e.g., vi…
GR-LLMs: Recent Advances in Generative Recommendation Based on Large Language Models
Zhen Yang, Haitao Lin, Jiawei xue +1
In the past year, Generative Recommendations (GRs) have undergone substantial advancements, especially in leveraging the powerful sequence modeling and reasoning capabilities of La…