We present AXIS, a web-based, community-driven infrastructure for scalable manipulation data collection through browser-based MuJoCo-WASM teleoperation and automated task generation. We build a growable manipulation dataset comprising 207 tasks, 50K+ trajectories, together with a unified data processing pipeline for trajectory standardization, success validation, quality filtering, temporal smoothing, resampling, IsaacSim-based visual and physics augmentation, and versioned task snapshotting. We introduce a systematic evaluation protocol for studying policy learning and within-embodiment dataset scaling, including standardized benchmark comparisons and controlled scaling experiments using the AXIS-25%/50%/100% dataset snapshots under a fixed held-out evaluation suite.
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. Each trajectory contains task metadata, robot embodiment, environment identifier, simulator version, timestamps, observations, robot states, actions, and success information. Optional fields include object states, camera streams, segmentation markers, and failure labels when available. This format makes demonstrations replayable, filterable, and compatible with downstream policy learning.
The AXIS Franka Dataset
Validation and refinement. To ensure data quality, all demonstrations undergo a standardized validation pipeline that verifies trajectory completeness, task success, and physical consistency. Valid trajectories are subsequently smoothed and resampled to a unified control frequency, improving temporal consistency and reducing teleoperation noise. The refinement process substantially reduces acceleration and jerk while maintaining a high replay success rate. Additional implementation details are provided in the Appendix.
Data augmentation. To improve diversity and robustness, AXIS incorporates augmentation through spatial and visual randomization. These augmentations generate additional valid scene configurations while preserving task semantics and success conditions. The resulting variations cover changes in object layouts, viewpoints, textures, lighting, and backgrounds, expanding the dataset to more than 60K scene variants. This diversity makes AXIS particularly useful for studying robustness and generalization under environmental variations.
The benchmark supports two complementary evaluation modes. First, AXIS enables direct comparison across representative policy families, including conventional visuomotor imitation learning methods and vision-language-action policies. Second, AXIS supports within-embodiment scaling studies, where policies are trained on AXIS-25%, AXIS-50%, AXIS-100%, and future larger snapshots to measure whether additional task coverage improves held-out manipulation generalization. Together, these properties position AXIS not only as a large-scale manipulation dataset, but also as a long-term benchmark for studying scalable robot learning and data-driven generalization.
Experimental Setup
AXIS dataset. The AXIS training corpus is collected through the browser-based MuJoCo-WASM teleoperation frontend and stored in the unified AXIS trajectory format. The robot embodiment is a Franka Research 3 robot arm with a parallel-jaw gripper; tasks cover tabletop manipulation behaviors including pick-and-place, stacking and sorting, articulated object interaction, pushing, pouring, and tool-use-style manipulation. Raw demonstrations are passed through the AXIS processing pipeline (success validation, static-segment removal, Savitzky-Golay smoothing, fixed-rate resampling) and then replayed in IsaacSim under randomized visual and physical conditions. We use the AXIS-100% snapshot as the largest continual pretraining pool, and sample uniformly without replacement at the task level to construct the 25% and 50% subsets used in scaling experiments.
Evaluation protocol. All policies are evaluated on the LIBERO-Plus robustness suite, which extends LIBERO with systematic perturbations along seven axes (Camera, Light, Sensor Noise, Background, Layout, Language, Robot). For each (task, perturbation axis) pair we fix the rollout budget to a constant number of rollouts across conditions, and report mean success rate over the rollouts. As a within-distribution sanity check, we additionally report success rate on the held-out task split of AXIS-100% itself; this confirms that AXIS continual pretraining does not destructively shift the policy away from the AXIS task distribution.
Discussion
Why growable datasets matter. Static datasets are valuable for benchmarking, but robot manipulation learning also benefits from data sources that can grow with model capability and observed failures. AXIS turns data collection into an iterative feedback loop: our task generation module expands semantic diversity through new manipulation problems, community teleoperation adds behavioral diversity through varied human strategies and corrections, and visual and physics augmentation expands environmental diversity through changes in appearance, scene configuration, and dynamics. In this sense, dataset growth becomes a mechanism for improving policy robustness rather than a one-time release process.
Community collection and data quality. Community-driven collection improves scale and accessibility, but introduces significant variation in operator skill, motion smoothness, strategy, and task completion quality. Useful variation exposes policies to diverse grasps, recoveries, and correction patterns, while harmful variation appears as corrupted states, idle segments, failed rollouts, discontinuities, or unstable actions. AXIS addresses this tradeoff by making validation, cleaning, smoothing, replay, and augmentation core stages of dataset construction, unifying broad participation with the quality control needed for downstream policy learning.
Conclusion
We introduced AXIS, a growable community-driven data engine and benchmark for scalable learning for robot manipulation. AXIS combines browser-based MuJoCo-WASM teleoperation, automated task generation, distributed demonstration collection, standardized trajectory processing, IsaacSim-based augmentation, and fixed-protocol evaluation. It constructs a training-ready dataset that can expand through new tasks, demonstrations, and augmented conditions, while enabling controlled comparisons across vision-language-action policies. More broadly, AXIS points toward continuously extensible robot data pipelines that connect task generation, community collection, policy training, and failure-driven improvement.
Limitations and Future Work. AXIS currently focuses on simulated Franka tabletop manipulation, and sim-to-real transfer remains an open challenge. Future work will expand AXIS to include more robot embodiments, richer sensing modalities, longer-horizon tasks, finer-grained annotations, and active failure-driven data collection to support more generalizable and scalable robot learning.
Frequently Asked Questions
What is the AXIS data engine and how does it enable scalable robot manipulation data collection? AXIS is a web-based, community-driven infrastructure that uses browser-based MuJoCo-WASM teleoperation and automated task generation to collect scalable manipulation data from over 70,000 community members.
How does AXIS ensure data quality despite coming from diverse human operators? The system applies a standardized validation pipeline that verifies trajectory completeness, task success, and physical consistency, followed by smoothing, resampling, and data augmentation to address variation in operator skill and motion smoothness.
What robot tasks and embodiments does the AXIS Franka dataset cover? The dataset focuses on the Franka Research 3 robot arm with a parallel-jaw gripper, covering 207 tabletop manipulation tasks including pick-and-place, stacking, sorting, articulated object interaction, pushing, pouring, and tool-use-style behaviors.
How is the AXIS benchmark designed for evaluating policy learning and scaling? The benchmark supports direct comparison across policy families and within-embodiment scaling studies using AXIS-25%, 50%, and 100% snapshots, with evaluation on the LIBERO-Plus robustness suite across seven perturbation axes.