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Object rearrangement has recently emerged as a key competency in robotmanipulation, with practical solutions generally involving object detection,recognition, grasping and high-level planning. Goal-images describing a desiredscene configuration are a promising and increasingly used mode of instruction.A key outstanding challenge is the accurate inference of matches betweenobjects in front of a robot, and those seen in a provided goal image, whererecent works have struggled in the absence of object-specific training data. Inthis work, we explore the deterioration of existing methods' ability to infermatches between objects as the visual shift between observed and goal scenesincreases. We find that a fundamental limitation of the current setting is thatsource and target images must contain the same $\textit{instance}$ of everyobject, which restricts practical deployment. We present a novel approach toobject matching that uses a large pre-trained vision-language model to matchobjects in a cross-instance setting by leveraging semantics together withvisual features as a more robust, and much more general, measure of similarity.We demonstrate that this provides considerably improved matching performance incross-instance settings, and can be used to guide multi-object rearrangementwith a robot manipulator from an image that shares no object$\textit{instances}$ with the robot's scene.

Semantically Grounded Object Matching for Robust Robotic Scene Rearrangement