这篇论文提出了新方法 OTF，能在有干扰的场景下解耦动作源。OFT-LAM 和 OFT-LAM-Dino 两种变体在零样本迁移和复杂环境下表现不错，适合做多物体交互推理的研究者看看。
Observed Transition Factorization (OTF) 将每个过渡分解为稀疏的观察过渡原语，用于解耦智能体动作与干扰物、相机动态等。基于此，OTF-LAM 在标准逆向正向动力学框架中将运动原语抽象为动作潜变量，而 OTF-LAM-Dino 则在冻结的 DINOv2 表示空间中预测未来状态，无需解码器。实验表明，OTF 原语在控制载体和形态变化下零样本迁移，下游策略学习性能在复杂过渡歧义下匹配或优于基线。
Latent Actions from Factorized Transition Effects under Agent Ambiguity
Latent Action Models (LAMs) learn action-like proxies from observation transitions. However, in multi-object or distractor-rich scenes, these visual effects mix agent motion with distractors, camera dynamics, and background changes, making the underlying action source ambiguous without supervision. Structuring this mixture as reusable transition effects provides an intermediate representation from which action-like latents can be more robustly formed. We introduce Observed Transition Factorization (OTF), which decomposes each transition into a sparse set of observed transition primitives. Using these primitives as the transition interface, we propose OTF-LAM, which abstracts motion primitives into action-like latents within the standard inverse-forward dynamics framework, and OTF-LAM-Dino, a decoder-free variant that predicts future states in a frozen DINOv2 representation space. Empirically, OTF primitives transfer zeroshot across controlled carrier and morphology shifts, showing reusability. Furthermore, downstream policy learning results match or outperform baselines under complex transition ambiguity.