以潜在动作作为意图，实现世界动作模型的高效未来想象

摘要

世界动作模型（WAMs）通过建模观测的演化过程来提升机器人控制性能，但在测试时生成未来观测会带来显著的延迟。Fast-WAM省去了这一过程以提高效率；然而，我们的对照实现表明，Fast-WAM的泛化能力低于考虑未来信息的替代方案，尤其是在机器人演示数据稀缺和分布外场景下。为弥合这一差距，我们提出了**LAWA**，一种WAM架构，利用紧凑的潜在动作作为未来意图的可操作表示，从而在无需生成未来观测的情况下实现高效的测试时未来想象。具体而言，一个通过免动作预训练增强的离散分词器生成以操作为中心的码本目标。LAWA将锚定于这些目标的连续潜在状态与可执行动作块联合去噪，同时在推理时省略未来视频分支。在RoboCasa上，LAWA在少样本和全量数据设置下分别取得了65.6%和80.8%的最先进平均成功率，相较于对照的Fast-WAM基线分别提升了9.6和4.5个百分点。同时，它保持了与对照的Joint-WAM变体相当的性能水平，而推理延迟降低了42.9%。LAWA还在LIBERO-Plus上展现了具有竞争力的零样本鲁棒性，并在真实世界任务中表现出更优的性能。这些结果表明，未来想象不应被舍弃：以紧凑潜在动作保留这一能力，可以在性能、泛化和延迟之间实现有效的权衡。代码和模型将公开发布。

English

World action models (WAMs) improve robot control by modeling how observations evolve, but generating future observations at test time incurs substantial latency. Fast-WAM removes this process for efficiency; however, our matched implementations show lower generalization for Fast-WAM than for future-aware alternatives, especially with scarce robot demonstrations and in out-of-distribution scenarios. To bridge this gap, we introduce **LAWA**, a WAM architecture that uses compact latent actions as an operational representation of future intentions, enabling efficient test-time future imagination without generating future observations. Specifically, a discrete tokenizer enhanced by action-free pre-training produces manipulation-centric codebook targets. LAWA jointly denoises a continuous latent state anchored to these targets with executable action chunks while omitting the future-video branch at inference. On RoboCasa, LAWA achieves state-of-the-art average success rates of 65.6% and 80.8% in the few-shot and full data settings, improving over the matched Fast-WAM baseline by 9.6 and 4.5 points, respectively. It also preserves the performance level of the matched Joint-WAM variant while requiring 42.9% lower inference latency. LAWA also demonstrates competitive zero-shot robustness on LIBERO-Plus and superior performance on real-world tasks. These results show that future imagination need not be discarded: retaining it with compact latent actions yields an effective trade-off among performance, generalization, and latency. Code and models will be released.