DROP-Op: Generalized Passive Response for Object Dynamics Prediction

Predicting object dynamics from sparse interactions requires a representation that captures how responses vary across space and actions. We introduce generalized passive response (GPR), an action-to-displacement response map defined for a given initial state and interaction interface. DROP-Op identifies a finite-dimensional GPR state by fitting spatially varying coefficients in a learned response basis. The fit combines geometry-based evidence sharing with response-dependent regularization, and the identified state is reused to predict responses to new actions under compatible conditions. We evaluate DROP-Op on a simulated grasping benchmark with individual and combined shifts in geometry, material properties, actions, and grippers. On the 46-panel confirmation set, DROP-Op reduces mean endpoint and translation-centered displacement errors by 31.6% and 30.9%, respectively, relative to ALPaCA-MSE, the baseline with the lowest mean endpoint error. Matched attention and pooled-identification controls also yield higher prediction errors. Additional backbone and external-task studies show task-dependent benefits of adapting the identification procedure. Reusing the identified state amortizes identification cost over repeated queries.