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Human environments contain numerous objects configured in a variety of arrangements. Our goal is to enable robots to repose previously unseen objects according to learned semantic relationships in novel environments. We break this problem down into two parts: (1) finding physically valid locations for the objects and (2) determining if those poses satisfy learned, high-level semantic relationships. We build our models and training from the ground up to be tightly integrated with our proposedarXiv:2108.12062v1 fatcat:l53iivf37veyxe4i2tfhcwueca