Training data

Robot Learning Data Pipeline

Fourteen records retain their original units and task scope. Hours, trajectories, robots and simulation environments are not plotted on a shared scale.

Source observations through 2026-07-31. Analysis revised separately.Open all humanoid data charts

Data summary

What is included

Fourteen records retain their original units and task scope. Hours, trajectories, robots and simulation environments are not plotted on a shared scale.

Visible rows

14

Dataset scope

TechniaHQRobot source-linked dataset

Interactive infographic

Robot Learning Data Pipeline

Fourteen records retain their original units and task scope. Hours, trajectories, robots and simulation environments are not plotted on a shared scale.

  1. Figure Helix multi-robot teleoperation dataset

    Recorded quantity
    500 hours
    Scope
    Multi-robot, multi-operator teleoperation data
    Robot
    Figure 02

    Published for the original Helix system.

    Read source ↗
  2. Figure Helix logistics training subset

    Recorded quantity
    10 hours
    Scope
    Moving-conveyor package manipulation
    Robot
    Figure 02

    One of four controlled training-volume conditions in the scaling study.

    Read source ↗
  3. Figure Helix logistics training subset

    Recorded quantity
    20 hours
    Scope
    Moving-conveyor package manipulation
    Robot
    Figure 02

    One of four controlled training-volume conditions in the scaling study.

    Read source ↗
  4. Figure Helix logistics training subset

    Recorded quantity
    40 hours
    Scope
    Moving-conveyor package manipulation
    Robot
    Figure 02

    One of four controlled training-volume conditions in the scaling study.

    Read source ↗
  5. Figure Helix logistics training subset

    Recorded quantity
    60 hours
    Scope
    Moving-conveyor package manipulation
    Robot
    Figure 02

    One of four controlled training-volume conditions in the scaling study.

    Read source ↗
  6. Figure Helix 02 human-motion retargeting

    Recorded quantity
    1,000 hours
    Scope
    Human motion data used for full-body control
    Robot
    Figure 03

    The source states more than 1,000 hours.

    Read source ↗
  7. Figure Helix 02 parallel simulation

    Recorded quantity
    200,000 parallel environments
    Scope
    Simulation environments used for locomotion/control training
    Robot
    Figure 03

    Environment count is not a trajectory or hour count.

    Read source ↗
  8. AgiBot World embodied dataset

    Recorded quantity
    1,000,000 trajectories
    Scope
    More than 1,000 tasks collected with more than 100 robots
    Robot
    Five robot types

    Trajectories are not equivalent to hours or validated task successes.

    Read source ↗
  9. AgiBot World collection fleet

    Recorded quantity
    100 robots
    Scope
    Collection fleet lower bound
    Robot
    Five robot types

    The source states more than 100 robots.

    Read source ↗
  10. AgiBot World task coverage

    Recorded quantity
    1,000 tasks
    Scope
    Task coverage lower bound
    Robot
    Five robot types

    The source states more than 1,000 tasks.

    Read source ↗
  11. Fourier ActionNet bimanual teleoperation dataset

    Recorded quantity
    30,000 trajectories
    Scope
    Bimanual teleoperation trajectories
    Robot
    Fourier humanoid platforms

    The source states more than 30,000 trajectories.

    Read source ↗
  12. 1X egocentric human video

    Recorded quantity
    900 hours
    Scope
    Egocentric human demonstrations
    Robot
    NEO/EVE learning stack

    Different modality from robot teleoperation; do not add without a unit-aware breakdown.

    Read source ↗
  13. 1X robot fine-tuning data

    Recorded quantity
    70 hours
    Scope
    Robot fine-tuning data
    Robot
    NEO/EVE learning stack

    Reported separately from human video and unfiltered robot data.

    Read source ↗
  14. 1X unfiltered robot data

    Recorded quantity
    400 hours
    Scope
    Unfiltered robot data
    Robot
    NEO/EVE learning stack

    Reported separately from fine-tuning data.

    Read source ↗

Accessible data table

The table contains the same source-linked records used by the chart. It is the reference when labels overlap or a visual scale compresses values.

Source-linked data used in the infographic
Pipeline stageOrderDocumented volumeUnitStatusEvidenceSource
Figure Helix multi-robot teleoperation dataset1500hoursofficially-documentedOfficial announcementFigure Helix official technical articleChecked 2026-07-31
Figure Helix logistics training subset210hoursofficially-documentedOfficial announcementFigure official Helix logistics scaling studyChecked 2026-07-31
Figure Helix logistics training subset220hoursofficially-documentedOfficial announcementFigure official Helix logistics scaling studyChecked 2026-07-31
Figure Helix logistics training subset240hoursofficially-documentedOfficial announcementFigure official Helix logistics scaling studyChecked 2026-07-31
Figure Helix logistics training subset260hoursofficially-documentedOfficial announcementFigure official Helix logistics scaling studyChecked 2026-07-31
Figure Helix 02 human-motion retargeting31000hoursofficially-documentedOfficial announcementFigure Helix 02 official technical articleChecked 2026-07-31
Figure Helix 02 parallel simulation4200000parallel environmentsofficially-documentedOfficial announcementFigure Helix 02 official technical articleChecked 2026-07-31
AgiBot World embodied dataset51000000trajectoriesofficially-documentedOfficial announcementAgiBot official research pageChecked 2026-07-31
AgiBot World collection fleet5100robotsofficially-documentedOfficial announcementAgiBot official research pageChecked 2026-07-31
AgiBot World task coverage51000tasksofficially-documentedOfficial announcementAgiBot official research pageChecked 2026-07-31
Fourier ActionNet bimanual teleoperation dataset630000trajectoriesofficially-documentedOfficial announcementFourier official teleoperation and ActionNet documentationChecked 2026-07-31
1X egocentric human video7900hoursofficially-documentedOfficial announcement1X official world-model training articleChecked 2026-07-31
1X robot fine-tuning data870hoursofficially-documentedOfficial announcement1X official world-model training articleChecked 2026-07-31
1X unfiltered robot data8400hoursofficially-documentedOfficial announcement1X official world-model training articleChecked 2026-07-31

Methodology

How the dataset is built

Hours, trajectories, robots, tasks and simulation environments are different units and are never summed into one volume. Each row preserves the collection modality and scope stated by the source.

Editorial analysis

The records describe different training resources

The table combines teleoperation, human-motion data, simulated environments and embodied datasets. An hour measures duration. A trajectory measures an episode. A fleet count describes collection hardware. None can be converted to another without additional information.

Subsets can overlap larger collections

The four Figure logistics training subsets belong to one scaling study. They should not be added as independent datasets without establishing that their episodes are disjoint. Likewise, a robot count and task count can describe the same collection rather than extra training volume.

Volume leaves quality unresolved

For a training decision, inspect task coverage, recording quality, action labels and compatibility with the target robot. A larger number of trajectories can still contain repetitive or unsuitable examples. This page organizes the reported resources without assigning a universal data-quality score.

Frequently asked questions

Why is there no largest-dataset leaderboard?

The records use incompatible units and may overlap. A single numerical ranking would conceal those differences.

Sources

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