研究潜空间模型如何应对部分可观测的安全约束，发现并修复两类控制失效问题。
How Well Do Latent World Models Understand Partially Observable Safety Constraints?
- 通过信息量与滚动预测诊断安全可观测性与可预见性
- 在烹饪任务中提升机械臂安全性，但略增保守性
- 适合关注机器人安全控制的开发者与研究者
潜空间世界模型从高维观测中学习状态表示与动态，适用于难以建模的机器人控制场景。然而，控制性能取决于潜状态是否包含任务所需信息。本文研究潜空间安全控制问题，揭示当安全相关信息未被保留时，部分可观测性会引发控制失败。具体识别出两类模型失效模式：估计缺口（当前观测无法揭示安全关键量，如烹饪中的温度）和预测缺口（故障发生后可察觉，但无法从现有观测可靠预判）。为此提出两种诊断方法：基于互信息的安全可观测性度量，以及基于滚动的未来安全可预测性度量。针对每种失效模式，分别提出缓解策略：特权多模态监督用于估计缺口，共形风险校准用于预测缺口。在两个硬件案例研究中（使用单模态RGB与多模态RGB+触觉、RGB+热成像模型），验证了这些策略显著提升了Franka Research 3机械臂在部分可观测挑战性烹饪任务中的安全性，尽管伴随一定程度的保守性增加。本工作进一步引发对世界模型状态表征是否足以实现可靠机器人控制的思考。
原文摘要 · Abstract (English)
Latent world models are a promising approach for learning state representations and dynamics directly from high-dimensional observations, enabling robot control in hard-to-model settings. However, control performance ultimately depends on the latent representation encoding the required information for the task. In this work, we study latent-space safe control problems and show how partial observability can induce control failures when safety-relevant information is not preserved in the latent state. Specifically, we identify two world model failure modes: estimation gaps, where current observations do not reveal safety-critical quantities (e.g., temperature in a cooking task), and prediction gaps, where failures are observable once they occur but cannot be reliably anticipated from available observations. We introduce two diagnostics for these gaps: a mutual-information-based measure of safety observability and a rollout-based measure of future safety predictability. Finally, we present mitigation strategies for each failure mode: privileged multimodal supervision for estimation gaps and conformal risk calibration for prediction gaps. Across two hardware case studies -- using unimodal RGB world models and multimodal RGB+Tactile and RGB+Thermal variants -- we show that these mitigation strategies improve the safety of a Franka Research 3 manipulator on challenging cooking tasks under partial observability, albeit with increased conservativeness. More broadly, our work raises the question of when world model state representations are sufficient for reliable robot control
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