外观
论文arXiv 2610.0759922 天前
为世界模型中的鲁棒决策建模潜空间扰动
研究世界模型潜空间鲁棒决策，通过构造并校准潜在动力学扰动，在仿真和Franka真机策略引导中验证。
Franka FR3原文链接：Modeling Latent Disturbances for Robust Decision-Making in World Models（arXiv 2610.07599 · 发布日期：2026-10-06）
Modeling Latent Disturbances for Robust Decision-Making in World Models
研究世界模型潜空间鲁棒决策，通过构造并校准潜在动力学扰动，在仿真和Franka真机策略引导中验证。
核心要点
- 世界模型潜空间由观测学习而来，难以定义能反映系统不确定性的扰动。
- 将扰动建模为潜在动力学扰动，结合相似度、分布外检测与保形预测校准不确定集。
- 通过博弈优化联合求解鲁棒动作与最坏扰动，用于安全过滤和生成式控制策略引导。
- 仿真显示可在潜空间决策，Franka真机失败率降低70%和54%；摘要给出项目网页。
验证范围与局限：验证包括受控仿真实验，以及Franka机械臂上的安全过滤和采样式策略引导硬件实验。
英文摘要 (Abstract)
In this paper, we study robust decision-making in the latent space of world models (WMs). Robust optimization is a mathematical framework where, given explicitly specified dynamics and physically meaningful disturbances, a robot can select actions that remain effective even under worst-case disturbances. However, applying this principle to the learned latent space of WMs introduces a fundamental challenge: because WMs have fully learned state spaces and dynamics inferred from high-dimensional observations, it is unclear how to define latent-space disturbances that faithfully represent uncertainty in the underlying system. Our key idea is to model a latent-space disturbance as a perturbation to the learned latent dynamics that induces pessimistic but plausible transitions. Specifically, we construct a set of plausible latent dynamics by combining a dynamics-aware similarity metric that captures plausible transitions with out-of-distribution detection that excludes implausible latent states. We calibrate this uncertainty set over latent dynamics using conformal prediction, ensuring that WM imaginations induced by the latent disturbance remain plausible without becoming overly pessimistic. We then jointly optimize robust robot actions and the worst-case latent disturbances through game-theoretic optimization. We leverage this latent-space robust optimization to robustify policy steering, considering two paradigms: latent safety filtering and sample-and-verify steering of a generative control policy. Our controlled simulation experiments show that our latent disturbance enables robust decision-making directly in WM latent spaces, and hardware experiments with a Franka manipulator show that modeling latent disturbances enables robust policy steering, reducing failures by 70% in safety filtering and 54% in sampling-based policy steering. Project website: https://junwon.me/LatentDisturbance/.
作者：Junwon Seo, Andrea V. Bajcsy
来源
本页事实对应的公开页面，以原文为准。