机器人手腕的“预知”能力
现在的机器人操作模型把主摄像头和手腕摄像头当两个平行输入，但真正精细的操作(比如拧螺丝、插线)靠的是手腕视角下“接下来会发生什么”。这篇 W2-VLA 让模型先根据任务指令和当前画面，预测手腕在未来几帧会看到什么，再基于这个“预知”来生成动作。相当于给机器人加了个“手部慢动作预演”。在 LIBERO、RoboTwin 和真实操作测试中，精细和接触敏感的操作成功率都提升了，而且动作生成速度保持在 80Hz 以上，够快。
📄 原文摘要(英文)
Vision-language-action (VLA) models often treat main-view and wrist-view observations as parallel visual inputs, overlooking their distinct roles in robot manipulation. Fine-grained manipulation, however, benefits from anticipating how wrist-local interactions may evolve under the global task context. To address this limitation, we present World-to-Wrist VLA (W2-VLA), a VLA model for fine-grained robot manipulation with task-conditioned future wrist modeling. Given current multi-view observations and a task instruction, W2-VLA contextualizes a set of latent modeling tokens as a compact interface between the vision-language model and the wrist predictor. Conditioned on this interface and the observed wrist history, the predictor forecasts future wrist latents, which are transformed into future-aware context for action prediction. In addition, we introduce W2-CoT, a synthesis pipeline that produces structured annotations describing manipulation progress, physical transition cues, and wrist-local evidence. These annotations provide auxiliary supervision that shapes the task-conditioned latent interface. Experiments on LIBERO, RoboTwin 2.0, and real-world manipulation tasks demonstrate improved fine-grained and contact-sensitive manipulation across both single-arm and bimanual settings, while maintaining action-generation rates above 80 Hz.