AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation
For robot learning researchers, AXIS provides a scalable, community-driven platform for data collection and benchmarking, addressing the bottleneck of diverse demonstration acquisition.
AXIS introduces a community-driven data engine for scalable robot manipulation, enabling browser-based teleoperation and automated task generation. The dataset includes 207 tasks and 50K+ trajectories, and continual pretraining on AXIS improves the success rate of π0.5 by 5.8% and outperforms RoboCasa365 by 37.3%.
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.