量子位·2025-10-29 09:30Core Insights - WorldVLA is a unified framework that integrates Visual Language Action Models (VLA) with World Models, proposed by Alibaba DAMO Academy, Lake Lab, and Zhejiang University [1][4] - Experimental results indicate that WorldVLA significantly outperforms independent action models and world models, showcasing a mutual enhancement effect [2] Model Overview - The framework combines three independent tokenizers for encoding images, text, and actions, utilizing a VQ-GAN model for image tokenization with a compression ratio of 16 and a codebook size of 8192 [8] - The action tokenizer discretizes continuous robot actions into 256 intervals, representing actions with 7 tokens [8] Model Design - WorldVLA employs a self-regressive action world model to unify action and image understanding and generation [4] - The model addresses limitations of existing VLA and world models by enhancing action generation accuracy through environmental physical understanding [5][14] Training and Performance - WorldVLA is jointly trained by integrating data from both action models and world models, enhancing action generation capabilities [13] - The model's performance is positively correlated with image resolution, with 512x512 pixel resolution showing significant improvements over 256x256 [21][23] Benchmark Results - WorldVLA demonstrates superior performance compared to discrete OpenVLA models, even without pre-training, validating its architectural design [19] - The model's ability to generate coherent and physically plausible states in various scenarios is highlighted, outperforming pure world models [31][32] Mutual Enhancement - The world model enhances the action model's performance by predicting environmental state changes based on current actions, crucial for tasks requiring precision [25] - Conversely, the action model improves the visual understanding of the world model, supporting better visual generation [17][30]