Newest papers

De-Diffusing WAMs: Action-Sufficient Direct Prediction for Robot Control

Shaoyi Zheng, Zhongtao Jiang, Yue Peng, Wenbo Lu, Shunchi Zhang, Xi Wang, Shengji Wan · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28122 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

这种缓慢的世界动作模型(WAMs)使用预测的视觉未来来条件机器人行动,但继承了扩散受过训练的专家的昂贵的迭代推断。我们问一个单步预测的未来必须保留什么,以支持准确的单步动作预测。在评估的WAM中,重新采样视频噪音对下游动作的影响很小,但天真地将两条流转化为一个步骤的预测者,大大降低了任务的成功。视频空间监督仅部分地缩小了这个差距,而本地敏感性分析表明,视觉预测错误对行动准确性具有高度不平等的后果。这些发现激励了DirectWAM,这将预测的视频和行动专家转化为单步预测器,而无需

Machine translation (Meta M2M100); the English original follows.

Abstract

Cascade world-action models (WAMs) use predicted visual futures to condition robot actions, but inherit costly iterative inference from diffusion-pretrained experts. We ask what a single-step predicted future must preserve to support accurate single-step action prediction. In the evaluated WAM, resampling video noise has little effect on downstream actions, yet naively truncating both streams to one step substantially reduces task success. Video-space supervision only partially closes this gap, and a local sensitivity analysis shows that vision prediction errors have highly unequal consequences for action accuracy. These findings motivate DirectWAM, which converts pretrained video and action experts into single-step predictors without stochastic sampling through action-sufficient distillation, while retaining the explicit future-to-action cascade. After learning a one-step action model, we train the future predictor by decoding actions from its own predicted futures and backpropagating demonstration action error through the expert, with an optional future-latent reconstruction loss. The resulting objective evaluates the student-generated future through its downstream control consequences rather than relying on representation matching alone. DirectWAM achieves 97.1% average success across four LIBERO suites and 85.4% on RoboTwin 2.0, outperforming Flash-WAM at the same one-evaluation-per-stream budget. This advantage has also extended to three real-world tasks that we designed. Compared with the full iterative teacher, it reduces inference latency by $28.0\times$.

Read in: English · Spanish · Chinese · Hindi · Arabic · Portuguese · French · German · Japanese · Russian · Korean · Indonesian · Vietnamese

Topics: Computer science, Artificial intelligence, Inference, Machine learning, Task (project management), Action (physics)

Sources: OpenAlex (CC0), arXiv, Crossref. Agent Exchange