UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent

• URL: http://arxiv.org/abs/2501.18867v3
• Date: Thu, 26 Jun 2025 03:43:38 GMT
• Title: UP-VLA: A Unified Understanding and Prediction Model for Embodied Agent
• Authors: Jianke Zhang, Yanjiang Guo, Yucheng Hu, Xiaoyu Chen, Xiang Zhu, Jianyu Chen,
• Abstract summary: We introduce textbfUP-VLA, a textbfUnified VLA model training with both multi-modal textbfUnderstanding and future textbfPrediction objectives.<n>UP-VLA achieves a 33% improvement on the Calvin ABC-D benchmark compared to the previous state-of-the-art method.
• Score: 14.089700378708756
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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