Object Count Generalization Is an Interface Problem: Warping a Frozen Skill Instead of Retraining It

Reinforcement learning policies for robot manipulation degrade when the number of objects $N$ in the scene grows beyond the training range. This is a covariate shift that also changes the dimension of the state. We propose WAVE (Warping Actions and Views of an Expert), which extends a pretrained single-object skill to many objects without updating its weights. WAVE has three parts. First, a selection policy picks the one object the robot works on next. Second, a warping policy reads a local view of the scene around that object, whose size does not depend on $N$. It emits a multiplier and an offset for every quantity the skill reads and every action it outputs. Third, the skill acts under this affine warp. It is shown a scene that holds a single object, while the warp steers it around the objects it is not shown and edits the actions that are sent to the robot. Both the selection and warping policies are trained with PPO on the multi-object reward and use the skill as a black box, so WAVE is task-general and skill-agnostic. It requires only that the skill was trained on the same embodiment and that every object has a goal pose. We also analyze the probability that the composition places all $N$ objects within a budget of skill invocations that grows linearly in $N$. We evaluate WAVE on cube arrangement tasks with a gantry robot and with a Franka Panda arm. Trained with at most nine cubes, it generalizes zero-shot to several times its training count, on the gantry to scenes of up to thirty cubes. Published methods that learn multi-object manipulation show count generalization up to six objects. Fine-tuning the skill, learning from the same local view without it, and an entity transformer, even when distilled from the pretrained skill, all collapse within their training counts.