World-model-based imagine-then-act becomes a promising paradigm for robotic manipulation, yet existing approaches typically support either purely image-based forecasting or reasoning over partial 3D geometry, limiting their ability to predict complete 4D scene dynamics. This work proposes a novel embodied 4D world model that enables geometrically consistent, arbitrary-view RGBD generation: given only a single-view RGBD observation as input, the model imagines the remaining viewpoints, which can then be back-projected and fused to assemble a more complete 3D structure across time. To efficiently learn the multi-view, cross-modality generation, we explicitly design cross-view and cross-modality feature fusion that jointly encourage consistency between RGB and depth and enforce geometric alignment across views. Beyond prediction, converting generated futures into actions is often handled by inverse dynamics, which is ill-posed because multiple actions can explain the same transition. We address this with a test-time action optimization strategy that backpropagates through the generative model to infer a trajectory-level latent best matching the predicted future, and a residual inverse dynamics model that turns this trajectory prior into accurate executable actions. Experiments on three datasets demonstrate strong performance on both 4D scene generation and downstream manipulation, and ablations provide practical insights into the key design choices.

翻译：基于世界模型的“想象-行动”范式已成为机器人操作中颇具前景的方法，然而现有方法通常仅支持纯图像预测或对部分三维几何进行推理，限制了其预测完整4D场景动态的能力。本文提出一种新型具身4D世界模型，可实现几何一致、任意视角的RGBD生成：仅需输入单视角RGBD观测，模型便可想象其余视角，这些视角可被反投影并融合，从而构建跨时间的更完整三维结构。为高效学习多视角、跨模态生成，我们显式设计了跨视角与跨模态特征融合机制，共同促进RGB与深度间的一致性保持，并强制实现视角间的几何对齐。在预测之外，将生成的未来状态转化为动作通常依赖逆动力学模型，但该过程存在不适定性——同一状态转移可能对应多种动作解释。我们通过测试时动作优化策略解决此问题：该策略反向传播穿过生成模型以推断轨迹级潜在变量，使其最佳匹配预测的未来状态；同时结合残差逆动力学模型，将该轨迹先验转化为精确的可执行动作。在三个数据集上的实验证明了该方法在4D场景生成与下游操作任务中的优异性能，消融研究为关键设计选择提供了实践见解。