{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/up-vla-a-unified-understanding-and-prediction","title":"UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent","arxiv_id":"2501.18867","date":"2025-01-31","proceeding":null,"authors":["Jianke Zhang","Yanjiang Guo","Yucheng Hu","Xiaoyu Chen","Xiang Zhu","Jianyu Chen"],"abstract":"Recent advancements in Vision-Language-Action (VLA) models have leveraged pre-trained Vision-Language Models (VLMs) to improve the generalization capabilities. VLMs, typically pre-trained on vision-language understanding tasks, provide rich semantic knowledge and reasoning abilities. However, prior research has shown that VLMs often focus on high-level semantic content and neglect low-level features, limiting their ability to capture detailed spatial information and understand physical dynamics. These aspects, which are crucial for embodied control tasks, remain underexplored in existing pre-training paradigms. In this paper, we investigate the training paradigm for VLAs, and introduce \\textbf{UP-VLA}, a \\textbf{U}nified VLA model training with both multi-modal \\textbf{U}nderstanding and future \\textbf{P}rediction objectives, enhancing both high-level semantic comprehension and low-level spatial understanding. Experimental results show that UP-VLA achieves a 33% improvement on the Calvin ABC-D benchmark compared to the previous state-of-the-art method. Additionally, UP-VLA demonstrates improved success rates in real-world manipulation tasks, particularly those requiring precise spatial information.","url_abs":"https://arxiv.org/abs/2501.18867v2","url_pdf":"https://arxiv.org/pdf/2501.18867v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"robot-manipulation","task_name":"Robot Manipulation"},{"task_slug":"vision-language-action","task_name":"Vision-Language-Action"}],"methods":[{"method_slug":"focus","method_name":"Focus"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/robot-manipulation-on-calvin","task":"Robot Manipulation","dataset":"CALVIN","model":"UP-VLA","rank_in_archive_order":5,"of":19,"metrics":{"avg. sequence length (D to D)":"4.08"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2501.18867","atlas_url":"https://app.syntology.ai/?focus=2501.18867","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}