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

Mengfei Zhao, Dihong Huang, Yikai Tang, Peihao Li, Mingxuan Yan, Ruiqi Zhuang, Yanjia Huang, Jie Wang, Hai Zhai, Tony Zhou, Rui Zhang, Zhexi Luo, Yuchen Huang, Jianfei Yang, Jiachen Li · N/A · 2026

Summary

Abstract Summary

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.

Key Points

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.

Scalable Robot Data Collection

The dataset layer converts raw community demonstrations into training-ready robot trajectories through validation, filtering, refinement, and augmentation. Since demonstrations are collected from human operators rather than scripted controllers, the resulting data contains behavioral diversity in approach direction, grasp timing, correction behavior, and execution style.

Completed demonstrations are serialized into a unified trajectory format.

Scalable Robot Data Collection — AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation — Figure 2: Visualization of augmentation diversity for three example tasks. For each task (row), we show three augmented variations (columns) obtained by randomizing appearance, lighting, environment, and initial object positions. Across augmentations, we vary materials and textures (e.g., table, floor, objects), lighting conditions (e.g., brightness and shadows), background and scene layout (e.g., walls, furniture, decorations), and initial states (e.g., object positions and orientations). This process substantially increases data diversity and improves policy robustness to environmental variations.

The AXIS Franka Dataset

Dataset overview. AXIS is a large-scale tabletop manipulation dataset built around a Franka Research 3 robot equipped with a parallel-jaw gripper. As summarized in Fig. 3 , the current dataset snapshot contains 207 manipulation tasks, over 50K human demonstration trajectories, and more than 60K task or scene variants.

Each task is specified by a language instruction, a parameterized simulation scene, task assets, and a structured success checker.

The AXIS Franka Dataset — AXIS: A Growable Community-Driven Data Engine for Scalable Robot Manipulation — Figure 3: Overview of the AXIS dataset.

Experiments

To evaluate AXIS as a growable data engine, we organize our experiments around three questions. (i) Does continual pretraining $\pi_{0.5}$ [ 44 ] on AXIS improve downstream LIBERO-Plus [ 12 ] performance, and is the improvement specific to AXIS versus a matched-volume baseline drawn from RoboCasa365 [ 39 ] ?

(ii) Does the improvement scale with AXIS volume, and is it largest when the downstream LIBERO fine-tuning data is scarce? (iii) On which LIBERO-Plus perturbation axes does AXIS continual pretraining help most, and does the pattern match th...

Sources and Demonstrations

Figures are reproduced from Zhao et al. See the full paper for experimental details and the project page, when the paper links one, for demonstrations and videos.