Sensitivity Shaping for Latent Modeling

Generative dynamics models let robots plan behavior in rich, uncertain environments — but safely deploying them requires reliably detecting when the robot is about to enter unfamiliar territory. Existing out-of-distribution detection methods bolt on detectors after the fact, and this paper shows why that fails: if the dynamics model is locally insensitive to different control inputs in critical regions, unsafe actions can produce latent predictions that look like safe ones, suppressing the alert. The proposed fix — control-sensitivity regularization during training — makes the model more discriminating in exactly the regions where it matters. Applications include safer robot navigation in unstructured environments, robotic manipulation, autonomous vehicle planning, and any deployment where catastrophic failure must be caught before execution. Authors: Hongzhan Yu, Chenghao Li, Ruipeng Zhang, Henrik Christensen, Sicun Gao Paper: https://arxiv.org/abs/2606.14585v1