论文HF Daily Papers · arXiv 2610.420963 天前

CausalWAM：稀疏路由解耦动作因果变量提升机器人闭环成功率

针对世界动作模型（WAM）中动作相关潜变量与干扰变量纠缠问题，提出CausalWAM方法。通过增加稀疏路由路径约束，在无需额外时序假设下实现因果结构可识别，合成数据与真机实验验证了闭环任务效率提升。

AI 整理

仿真与训练
具身模型

本文由 AI 根据公开内容整理，可能有误，请以原文为准：CausalWAM: Learning the Causes of Actions in World Action Models（HF Daily Papers · arXiv 2610.42096）

核心内容

现有世界动作模型（WAM）虽能从观测中学习潜变量以预测场景演化并生成动作，但常将动作相关变量与无关干扰变量纠缠，导致模仿学习失效。研究团队提出CausalWAM，从理论层面证明在稀疏性约束下，即使动作相关潜变量与其他变量统计相关，生成动作的潜变量仍是真实因果变量的可逆函数，且无需对未观测机制演变做假设或依赖充分时序相关性。

工程实现上，CausalWAM在预训练WAM的动作头中嵌入稀疏路由路径，保持原有世界建模目标不变，强制模型仅利用动作相关潜变量生成控制指令。该方法在合成数据集及真实机器人平台上完成验证，结果表明其有效解耦了因果结构，显著提升了机器人在闭环控制任务中的执行效率与成功率，为具身智能模型的鲁棒性提供了新的理论支撑与工程范式。

关键看点

理论突破：证明在稀疏约束下，动作生成潜变量是真实因果变量的可逆函数，无需假设未观测机制演变或充分时序依赖。
架构改进：在预训练WAM动作头添加稀疏路由路径，保持世界建模目标不变，强制解耦动作相关变量与干扰变量。
实验验证：在合成数据与真实机器人平台测试，证实方法能准确识别因果结构，并提升闭环控制任务的执行效率与成功率。
核心痛点：解决传统WAM因潜变量纠缠导致模仿学习偏差的问题，确保动作生成仅依赖于演示中的真实因果因素。

工程与产业观察

该工作为世界模型驱动的具身智能提供了关键的理论修正与工程插件。通过稀疏路由解耦因果变量，解决了WAM在复杂场景中因特征纠缠导致的策略泛化难题。对于算法工程师而言，这是一种低成本改进预训练模型鲁棒性的有效手段；对产业界而言，提升闭环成功率直接关联机器人部署可靠性，加速了从仿真到真机的迁移验证进程。

英文摘要 (Abstract)

World action models (WAMs) learn latent variables from observations and use these to both predict the evolution of the scene and to generate actions. The prediction has to be of the whole scene, whereas only a portion of the scene causes actions. WAMs fitted to demonstrations do not, in general, guarantee that they will generate actions solely from the latent causes of the demonstrated actions (the action-relevant latent variables), as the model could just as well learn to imitate in this way whilst entangling the action-relevant latent variables with the nuisance variables. We therefore separate out what causes action from what prediction depends on. We prove that under a sparsity constraint the latent variables used to generate actions are an invertible function of the action-relevant latent variables alone and the true structure is identified up to a permutation of the latent variables. This requires neither sufficient temporal dependence nor any assumptions on how the unobserved regimes evolve and holds even when the action-relevant latent variables are statistically dependent on the rest. We therefore propose CWAM (Causal WAM), which incorporates the constraint into a pretrained WAM by adding a sparse routing path to its action head, with the world modeling objective remaining unchanged. Experiments on both synthetic data and real-world robots validate the theory as well as showcase the improved efficiency in closed-loop success.

Hugging Face Daily Papers 热度：3 upvotes 作者：Juntao Cheng, Jinzhou Tang, Fan Feng, Henrik I. Christensen, Yujia Zheng