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arXiv · 2607.27890

Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation

Woo Chul Shin·Zhenyang Chen·Alfred Cueva·Nadun Ranawaka Arachchige·Yingyan Celine Lin·Benjamin Joffe·Shreyas Kousik·Danfei Xu
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Abstract

Visuomotor policies have advanced on manipulation tasks where the target object stays static during execution, but real deployments break this assumption: parts drift on conveyors and fruits sway in the wind. We introduce Static In, Dynamic Out (SIDO), a counterfactual action augmentation that enables a policy trained only on static object demonstrations to adapt to unseen object motion at test time. Our key idea is to factorize moving object manipulation into two sub-problems: predicting where the object will be, and reaching that predicted pose. SIDO displaces the object to a counterfactual future position and morphs the demonstrated action chunk to preserve the hand-object relative pose, yielding a goal-conditioned policy. At deployment an object pose predictor supplies the future position. Across three simulated tasks (Mug, Square, Stack) under five object motion patterns and two real-world tasks (Gantry, Peachtree), SIDO improves moving object success over the baselines while preserving static object performance. Project website: https://sido-staticindynamicout.github.io/.

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Woo Chul Shin, Zhenyang Chen, Alfred Cueva, Nadun Ranawaka Arachchige, Yingyan Celine Lin, Benjamin Joffe, Shreyas Kousik, Danfei Xu. 2026-07-30. Static In, Dynamic Out: Counterfactual Action Augmentation for Moving Object Manipulation. https://arxiv.org/abs/2607.27890

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