AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation

• URL: http://arxiv.org/abs/2607.21588v1
• Date: Thu, 23 Jul 2026 17:58:08 GMT
• Title: AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation
• Abstract summary: AXIS is a growable community-driven data engine and benchmark for scalable robot learning.<n>It automatically generates and validates new manipulation tasks and transforms community-collected demonstrations into training-ready data.<n>Continual pretraining on AXIS substantially improves the overall success rate of $_0.5$ by 5.8%.<n>It exhibits consistent scaling with increasing data volume, with the largest gains observed under layout, sensor-noise, and camera perturbations.
• Score: 22.559633314919342
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning effective robot manipulation policies requires diverse, high-quality demonstrations, yet existing data pipelines are often difficult to scale because they rely on specialized hardware, centralized operators, or fixed task suites. We present AXIS, a growable community-driven data engine and benchmark for scalable robot learning, which enables browser-based teleoperation for large-scale demonstration collection, automatically generates and validates new manipulation tasks, and transforms community-collected demonstrations into training-ready data through automated success checking, quality filtering, trajectory smoothing, and visual and physics-based augmentation. The AXIS dataset currently contains 207 diverse tasks and 50K+ trajectories. Meanwhile, AXIS organizes data into task snapshots and evaluates policies with a systematic held-out protocol. We compare vision-language-action (VLA) policies under a unified AXIS evaluation suite and analyze scaling behavior across different data volumes. Continual pretraining on AXIS substantially improves the overall success rate of $π_{0.5}$ by 5.8%, outperforms the model pretrained on RoboCasa365 by 37.3%, and exhibits consistent scaling with increasing data volume, with the largest gains observed under layout, sensor-noise, and camera perturbations.

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