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DetAug: Obstacle-Blind Trajectory Augmentation for Zero-shot Obstacle Avoidance

arXiv · AI, language, vision and robotics · article · Sep 16, 2026 · UTC

Policies for robotic manipulation are produced by training on large teleoperated datasets. These datasets typically consist of free-space trajectories, making them difficult to transfer to test-time environments with obstacles. Previous methods for closing this gap have largely fallen into two groups. Dataset augmentation addresses it at training time but needs obstacle geometry in advance, whereas steering an existing checkpoint at inference time avoids that requirement but is limited in flexibility. Our method draws from both areas without inheriting either drawback. DetAug applies an obstac

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