Analysing When a Learned Subset of Objects Explains a Robot Policy: Discovery, Removal, Consequence

Object-centric robot policies promise to generalize across scenes and to reveal which objects a robot’s decisions rest on. We study In-Loop Subset Explanations (ILSE), policies that select a subset of objects inside the model, train the selec- tion end-to-end and present it as the explanation, and ask when this explanation is earned. We state three conditions for increasingly strong readings of the kept set: The selection must be discovered by the model rather than imposed (Discovery), excluded objects must be absent from the computation (Removal), and it must matter beyond a random subset of the same size (Consequence). We give a test for each condition and apply them to graph-based object-centric policies on three ma- nipulation benchmarks. Excluded objects still reach the action through standard operators, and correcting the removal lowers success by up to 24% relative on CALVIN, mostly through the reductions over excluded objects, and on one split turns an advantage over a pixel policy into a deficit. No tested design point satis- fies all three conditions: A labeled selector beats random selection at a matched count, but its choice is imposed by annotations, while a label-free selector that meets Removal and Discovery falls below random selection on average.