collaborators

11 papers

cs.RO2026

Embodied-R1.5: Evolving Physical Intelligence via Embodied Foundation Models

Yifu Yuan, Yaoting Huang, Xianze Yao +20

We introduce Embodied-R1.5, a unified Embodied Foundation Model (EFM) that integrates comprehensive embodied reasoning capabilities, spanning embodied cognition, task planning, cor…

cs.CV2026

VLM4VLA: Revisiting Vision-Language-Models in Vision-Language-Action Models

Jianke Zhang, Xiaoyu Chen, Qiuyue Wang +7

Vision-Language-Action (VLA) models, which integrate pretrained large Vision-Language Models (VLM) into their policy backbone, are gaining significant attention for their promising…

cs.CV2026

UAM: A Dual-Stream Perspective on Forgetting in VLA Training

Jianke Zhang, Yuanfei Luo, Yucheng Hu +6

Vision--language--action (VLA) models are typically built by fine-tuning a pretrained vision--language model (VLM) on action data. However, we show that this standard recipe system…

cs.RO2026

UniJEPA: Enhancing Robot Policy via Unified Continuous and Discrete Representation Learning

Jianke Zhang, Yucheng Hu, Yanjiang Guo +5

Building generalist robot policies that can handle diverse tasks in open-ended environments is a central challenge in robotics. To leverage knowledge from large-scale pretraining,…

cs.RO2026

Veo-Act: How Far Can Frontier Video Models Advance Generalizable Robot Manipulation?

Zhongru Zhang, Chenghan Yang, Qingzhou Lu +4

Video generation models have advanced rapidly and are beginning to show a strong understanding of physical dynamics. In this paper, we investigate how far an advanced video generat…

cs.RO2026

Realtime-VLA V2: Learning to Run VLAs Fast, Smooth, and Accurate

Chen Yang, Yucheng Hu, Yunchao Ma +3

In deployment of the VLA models to real-world robotic tasks, execution speed matters. In previous work arXiv:2510.26742 we analyze how to make neural computation of VLAs on GPU fas…