标题： VQ-VLA：通过扩展向量量化动作分词器改进视觉-语言-动作模型 显示英文标题

标题： VQ-VLA: Improving Vision-Language-Action Models via Scaling Vector-Quantized Action Tokenizers

摘要： 在本文中，我们引入了一个基于创新向量量化的行为标记器，该标记器建立在迄今为止最大规模的动作轨迹数据集之上，利用的数据量是之前方法的100多倍。 这个庞大的数据集使我们的标记器能够捕捉丰富的时空动态，从而生成一个不仅加速推理而且产生更平滑、更连贯动作输出的模型。 训练完成后，该标记器可以以零样本方式无缝适应各种下游任务，从短时域的反应行为到长时域的规划。 我们工作的关键发现是合成与真实动作轨迹之间的领域差距很小，这使我们在训练过程中能够有效利用大量合成数据而不影响实际性能。 为了验证我们的方法，我们在模拟环境和真实的机器人平台上进行了广泛的实验。 结果表明，随着合成轨迹数据量的增加，我们的标记器在下游任务上的性能显著提高——最明显的是，在长时域场景中的两个现实任务中成功率达到30%的提升。 这些发现突显了我们的动作标记器作为实时具身智能系统的一种强大且可扩展解决方案的潜力，为在不同应用领域中更高效和可靠的机器人控制铺平了道路。 项目网站：https://xiaoxiao0406.github.io/vqvla.github.io 显示英文摘要

摘要： In this paper, we introduce an innovative vector quantization based action tokenizer built upon the largest-scale action trajectory dataset to date, leveraging over 100 times more data than previous approaches. This extensive dataset enables our tokenizer to capture rich spatiotemporal dynamics, resulting in a model that not only accelerates inference but also generates smoother and more coherent action outputs. Once trained, the tokenizer can be seamlessly adapted to a wide range of downstream tasks in a zero-shot manner, from short-horizon reactive behaviors to long-horizon planning. A key finding of our work is that the domain gap between synthetic and real action trajectories is marginal, allowing us to effectively utilize a vast amount of synthetic data during training without compromising real-world performance. To validate our approach, we conducted extensive experiments in both simulated environments and on real robotic platforms. The results demonstrate that as the volume of synthetic trajectory data increases, the performance of our tokenizer on downstream tasks improves significantly-most notably, achieving up to a 30% higher success rate on two real-world tasks in long-horizon scenarios. These findings highlight the potential of our action tokenizer as a robust and scalable solution for real-time embodied intelligence systems, paving the way for more efficient and reliable robotic control in diverse application domains.Project website: https://xiaoxiao0406.github.io/vqvla.github.io