发表机构
Pennsylvania State University（宾夕法尼亚州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ATLAS通过对齐传输保留潜在关系几何并校准边际分布，提升世界模型在规划任务中的可靠性和新颖性泛化能力。
AI 中文摘要
潜在世界模型依赖表示几何进行规划，然而仅对潜在边际进行正则化并不能确定用于动作选择的状态到状态关系。我们表明，这可能导致规划相关的新颖性结构在表示被转换为规划器使用的最终潜在表示时被削弱。我们引入了潜在结构的对齐传输（ATLAS），这是一种训练目标，在校准全局潜在分布的同时显式保留关系几何。ATLAS将来自信息丰富的编码器表示的归一化成对结构传输到规划潜在表示，并通过一维Wasserstein-2传输使用Wasserstein嵌入匹配（WEMReg）来校准其边际。我们的分析表明，关系保留和边际校准施加了非冗余约束，并将有限候选规划稳定性与关系失真、潜在尺度不匹配和预测误差联系起来。在LeWM中实例化后，ATLAS在PushT、TwoRoom和OGBench-Cube上，在较低和较高新颖性评估子集上均提高了平均目标达成成功率，其中在较高新颖性的TwoRoom情节中增益最大。表示和展开诊断进一步显示，规划潜在表示中与新颖性相关的结构更强，边际校准得到改善，多步预测误差更低。总之，这些结果强调了保留规划相关潜在几何作为可靠世界模型规划的重要因素。代码可在以下网址获取：此https URL。
英文摘要
Latent world models rely on representation geometry for planning, yet regularizing the latent marginal alone does not determine the state-to-state relationships used for action selection. We show that this can cause planning-relevant novelty structure to be weakened as representations are transformed into the final latent used by the planner. We introduce Aligned Transport of Latent Structure (ATLAS), a training objective that explicitly preserves relational geometry while calibrating the global latent distribution. ATLAS transfers normalized pairwise structure from an informative encoder representation to the planning latent and uses Wasserstein embedding matching (WEMReg) to calibrate its marginal through one-dimensional Wasserstein-2 transport. Our analysis shows that relational preservation and marginal calibration impose non-redundant constraints, and connects finite-candidate planning stability to relational distortion, latent-scale mismatch, and prediction error. Instantiated in LeWM, ATLAS improves mean goal-reaching success across PushT, TwoRoom, and OGBench-Cube on both lower- and higher-novelty evaluation subsets, with the largest gain on higher-novelty TwoRoom episodes. Representation and rollout diagnostics further show stronger novelty-related structure in the planning latent, improved marginal calibration, and lower multi-step prediction error. Together, these results highlight preservation of planning-relevant latent geometry as an important ingredient for reliable world-model planning. Code is available at https://anonymous.4open.science/r/atlas-world-model-72C4/.
