{"ID":2921738,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-03T05:56:00.181519634Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01241","arxiv_id":"2606.01241","title":"OneVLA: A Unified Framework for Embodied Tasks","abstract":"Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroundings. However, current Vision-Language-Action (VLA) models remain constrained by task-specific architectures, specializing in either navigation or manipulation, which hinders the development of general-purpose robotic agents. To bridge this gap, we introduce OneVLA, a unified architecture that integrates these distinct tasks into a single, cohesive framework. Specifically, we design a unified action head capable of generating both navigation and manipulation actions without requiring task-specific variants. Furthermore, we propose a multi stage progressive training strategy-incorporating curated data construction and Chain-of-Thought (CoT) fine-tuning that facilitates strong positive transfer and mutual reinforcement between the two domains. Extensive experiments in both simulated and real-world environments demonstrate that OneVLA achieves state-of-the-art performance, significantly outperforming both specialized single-task and existing cross-task models. By unifying these core capabilities, OneVLA paves the way for truly general-purpose robotic systems. The model and source code will be publicly released.","short_abstract":"Navigation and manipulation are fundamental capabilities of embodied intelligence, enabling robots to interpret natural language commands and interact physically with their surroundings. However, current Vision-Language-Action (VLA) models remain constrained by task-specific architectures, specializing in either naviga...","url_abs":"https://arxiv.org/abs/2606.01241","url_pdf":"https://arxiv.org/pdf/2606.01241v1","authors":"[\"Lingfeng Zhang\",\"Xiaoshuai Hao\",\"Yingbo Tang\",\"Lei Zhou\",\"Shuyi Zhang\",\"Jinkun Liu\",\"Hongsheng Li\",\"Chenhao Zhang\",\"Qiang Zhang\",\"Hangjun Ye\",\"Xiaojun Liang\",\"Long Chen\",\"Wenbo Ding\"]","published":"2026-05-31T13:43:23Z","proceeding":"cs.RO","tasks":"[\"cs.RO\"]","methods":"[]","has_code":false}