新正则化法让机器人更安全
生成式动力学模型用于机器人规划，但需可靠检测策略导致的分布外(OOD)转换。现有方法将动力学视为固定并附加后验支持代理，但当动力学对关键动作选择局部不敏感时可能失败。本文提出支持条件控制敏感性正则化，在训练区域促进对控制输入的敏感响应，同时限制弱经验支持下的不稳定外推。在视觉避障、操作和真实机器人导航实验中，该方法提升了OOD检测和闭环规划安全性。
Sensitivity Shaping for Latent Modeling
Generative dynamics models enable planning in challenging robotic systems, but safe deployment requires reliably detecting policy-induced out-of-distribution (OOD) transitions. Existing methods typically treat the learned dynamics as fixed and attach post hoc support surrogates. We show that these surrogates can fail when the dynamics are locally insensitive to critical action choices: unsupported control actions may produce latent predictions that resemble demonstrated transitions, suppressing OOD signals despite large true predictive errors. To address this, we introduce support-conditioned control-sensitivity regularization, which promotes sensitive local response to control input changes in learned dynamics in high-support training regions. This preserves control-induced variation while limiting unstable extrapolation due to weak empirical support. Experiments in vision-based obstacle avoidance, manipulation, and real-robot navigation show improved OOD detection and safer closed-loop planning.