Sensitivity Shaping for Latent Modeling
The work addresses a failure where learned dynamics look normal even when they are wrong because they lack control sensitivity. The authors introduced support-conditioned control-sensitivity regularization to force learned dynamics to produce meaningful, non-trivial responses when controls are applied in areas with strong training data. This makes the model better at detecting out-of-distribution (OOD) transitions during planning.