机器人不再盲操作：先想后果再动手
现在的机器人操作模型，大多是「看到指令就出手」，并不预判动作会带来什么后果。这篇 WAVE-0 给机器人加了一个轻量的「世界模型」:先由大模型生成动作意图，再用一个 2.57 亿参数的小模型预测这个动作会带来什么状态，最后才让动作专家据此出手。它不需要昂贵的视频生成，也不用大规模视频预训练，直接拿真实机器人动作训练，靠一个「反事实排序」目标学会判断哪种未来更好。效果是：在 LIBERO 基准上成功率 98.7%，在真实机器人上把 π0.5 的成功率从 38.3% 提到 73.3%。它不是你明天就能装进自家机器人的东西，但方向很明确——让机器人像人一样，先想一步再动。
📄 原文摘要(英文)
Vision-language-action (VLA) policies excel at generalist robotic manipulation but generate actions without explicitly modeling their environmental consequences. World action models (WAMs) incorporate future prediction, yet most rely on costly video generation, while latent alternatives either use predicted futures only as auxiliary targets or infer latent actions from suboptimal visual transitions through costly large-scale video pretraining. We introduce WAVE-0 (World-model-Augmented VLA Execution), a framework that bridges VLAs and WAMs through a lightweight latent world model. By connecting the semantic understanding of pretrained VLAs with explicit future prediction, WAVE-0 allows anticipated action consequences to directly guide action generation. Built on a pretrained VLA, its 257M world model translates VLM-generated latent action plans into predicted future representations that condition the action expert, unifying latent planning, future prediction, and action generation. The world model is trained directly on ground-truth robot actions with a counterfactual ranking objective, requiring neither pixel reconstruction nor large-scale video pretraining. WAVE-0 achieves success rates of 91.47% on RoboTwin 2.0 and 98.7% on LIBERO, along with 88.0%, 61.4%, and 75.0% on LIBERO-Plus, LIBERO-Pro, and LIBERO-Para, respectively. On real-world tasks, it improves the success rate of π_{0.5} from 38.3% to 73.3%. These results demonstrate strong manipulation performance and generalization across diverse distribution shifts.