ActiveContact: Factorized Physical Hypotheses for Active Identification

Different physical worlds can produce similar motion, so one push is often not enough to know what is happening. We present Active Contact GS, which treats physical understanding as an active identification problem. The model keeps several possible physical worlds, then chooses the next probe that is expected to separate them best. The discrete world contains four directional contact factors and one global friction factor. Local factors take the states *native*, *rounded*, or *compliant*; the friction factor is *isotropic*, *x-grain*, or *y-grain*. This gives $3^5=243$ combinations, together with continuous parameters for mass, friction, restitution, and inertia. Expected information gain (EIG) scores each unused probe by how much it is expected to reduce uncertainty. With six probes, Active reaches **87.5%** exact-world accuracy on ID tasks, **84.6%** on unseen combinations, and **79.9%** under geometry shift, compared with 37.0%, 29.4%, and 35.7% for Random. On composition-OOD, five Active probes already reach 77.3%, above Random 29.4% and Fixed 32.0% with six probes. We also test two real-data interfaces. Physics101 is an external passive-video benchmark. Separately, we collect 72 tabletop push videos of a cube, sphere, and box; 69 are tracked successfully. In leave-one-repeat-out evaluation, EIG identifies the held-out object with 77.8% accuracy after four probes, compared with 53.6% for Random and 22.2% for Fixed. These results show that factorized physical hypotheses and information-seeking probes are useful when the correct explanation is not known in advance.