Nvidia(US:NVDA) 具身智能之心·2025-10-28 04:00Core Insights - The article presents VLA-0, a novel approach in the field of robot control that utilizes a vision-language-action model (VLA) without modifying the existing structure of the vision-language model (VLM) [1][2][3]. - VLA-0 demonstrates that a simple design can achieve top-tier performance, challenging the notion that complexity equates to better functionality in VLA development [14][21]. Summary by Sections Introduction to VLA-0 - VLA-0 breaks the conventional belief that more complex models yield better results by proposing a "zero modification" approach, allowing VLM to predict actions in text format without altering its architecture [1][2]. Current Challenges in VLA Development - Existing VLA models often sacrifice the inherent advantages of VLMs for added action functionalities, leading to issues such as increased complexity and reduced language comprehension [2][3]. Key Design Features of VLA-0 - VLA-0 retains the original VLM structure and focuses on optimizing input-output and training logic, allowing it to predict actions effectively [3][4]. - The input design includes system prompts, multi-modal observations, and natural language task instructions, ensuring that VLM can understand and process tasks without additional coding [4][5]. Action Decoding Mechanism - VLA-0 innovatively converts continuous actions into text that VLM can generate, enhancing action resolution and avoiding vocabulary conflicts [5][6]. - The training strategy employs masked action augmentation to improve the model's reliance on visual and task information rather than just text sequence continuity [7][8]. Experimental Results - VLA-0 outperforms complex models in both simulated and real-world scenarios, achieving an average success rate of 94.7% in simulations, surpassing all comparable models [10][11]. - In real-world tests, VLA-0 achieved a 60% success rate, significantly higher than the 47.5% of the SmolVLA model, demonstrating its effectiveness in practical applications [11][13]. Conclusions and Future Directions - The findings suggest that simpler designs can lead to superior performance in VLA development, emphasizing the importance of leveraging existing VLM capabilities [14][15]. - Future exploration may include large-scale pre-training, optimization of inference speed, and integration of 3D perception to enhance the model's adaptability and precision in complex environments [18][19][20].