Article

ReStruct: Guiding Robot Behavior at Inference Time via Physically-Aware Reconfiguration of Task Structure

ReStruct proposes a method to steer learned robot policies at inference time, enabling them to satisfy user preferences not specified during training without retraining. The method decomposes a visuomotor policy into a high-level state-machine skeleton and a low-level residual controller, and incorporates preferences into the skeleton via a synchronous product to reconfigure the task structure, while the controller remains frozen. Simulation and real-world experiments show that ReStruct can handle a variety of preferences, from object-level specifications to temporal-logic constraints, and after steering it outperforms existing methods in task success rate and preference adherence, leading VLA models by up to 25%.

Part of the event: ReStruct inference-time guidance for robot policies accepted to CoRL 2026

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