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Object rearrangement is the problem of enabling a robot to identify thecorrect object placement in a complex environment. Prior work on objectrearrangement has explored a diverse set of techniques for following userinstructions to achieve some desired goal state. Logical predicates, images ofthe goal scene, and natural language descriptions have all been used toinstruct a robot in how to arrange objects. In this work, we argue thatburdening the user with specifying goal scenes is not necessary inpartially-arranged environments, such as common household settings. Instead, weshow that contextual cues from partially arranged scenes (i.e., the placementof some number of pre-arranged objects in the environment) provide sufficientcontext to enable robots to perform object rearrangement \textit{without anyexplicit user goal specification}. We introduce ConSOR, a Context-awareSemantic Object Rearrangement framework that utilizes contextual cues from apartially arranged initial state of the environment to complete the arrangementof new objects, without explicit goal specification from the user. Wedemonstrate that ConSOR strongly outperforms two baselines in generalizing tonovel object arrangements and unseen object categories. The code and data canbe found at https://github.com/kartikvrama/consor.

ConSOR: A Context-Aware Semantic Object Rearrangement Framework for Partially Arranged Scenes