OpenBot
Platform
Egocentric datasetOpenReadiness 70/100Provisional

HumanPlus

HumanPlus is a Stanford full-stack system that lets a customized 33-DoF Unitree H1 humanoid shadow human body and hand motion in real time from RGB cameras, and then learn autonomous whole-body skills via behavior…

Source notes

HumanPlus is a Stanford full-stack system that lets a customized 33-DoF Unitree H1 humanoid shadow human body and hand motion in real time from RGB cameras, and then learn autonomous whole-body skills via behavior cloning on teleoperated demonstrations. The low-level shadowing policy is trained in simulation using the 40-hour AMASS human-motion dataset; the released task data consists of HDF5 imitation-learning episodes (ACT/Mobile-ALOHA style) recording two head-mounted egocentric RGB cameras plus 19-DoF body and dexterous-hand joint positions. Demonstrated skills include folding clothes, rearranging objects, warehouse unloading, two-robot greeting, wearing a shoe, and typing.

Scale
40 hours
Formats
hdf5
License
unknown
Published
2024-06-15
Official sourceAll recordsDataset index A–ZRun local preflightRequest scoped audit

Decision summary

Best for

teleoperation

Main blocker

Metadata requires review against the official source before publication.

Next check

Read the dataset manifest and feature schema.

Release history

Source-backed release timing for this canonical dataset record.

Important releases

  1. Dataset series published

    Release timing is recorded from official dataset metadata.

    Release evidence

Selection readiness

6 evidence dimensions for deciding whether this dataset is ready to inspect, compare, or adopt. This is not a model benchmark.

Catalog evidence · not task performance

How scores work

70/100

Provisional

3/6 dimensions scored · 42% confidence

Access and governance75conf. 85
Schema and signal coverageNot scoredconf. 15
Policy training readiness70conf. 55
World-model readinessNot scoredconf. 15
Failure and recovery readinessNot scoredconf. 15
Download and processing readiness65conf. 65
Read the evidence behind all 6 dimensions

Access and governance

Access and license are declared by the source.

usefulfit 75 · confidence 85

Schema and signal coverage

No machine-readable schema has been verified yet.

unknownnot scored · confidence 15

Policy training readiness

Observation and action/state signals are declared; alignment quality still depends on sample verification.

usefulfit 70 · confidence 55

World-model readiness

World-model observation, geometry, or temporal semantics are not verified.

unknownnot scored · confidence 15

Failure and recovery readiness

No verified failure/recovery annotation evidence is available yet.

unknownnot scored · confidence 15

Download and processing readiness

Scale is declared; transfer and processing estimates are not measured.

usefulfit 65 · confidence 65
Review unresolved evidence and next checks

Signal gaps

  • Gaze / attention. Not enough evidence is available to classify this signal.
  • Feedback / correction / failure. Not enough evidence is available to classify this signal.

Next checks

  • Read the dataset manifest and feature schema.
  • Run a bounded sample audit before assigning Strong readiness.
Engineering checks (OBRS) behind this record
Engineering checksBronze

Needs Audit

4/12checks passed

OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.

Standardization & Loaders8 / 25 pt
Physical & Action Quality8 / 25 pt
Semantic & Annotation12 / 20 pt
Real-World Validation0 / 15 pt
License & Compliance0 / 15 pt
Missing evidence and readiness gaps
  • A passing real-hardware test report is required.
  • A passing ingestion/pipeline test report is required.
  • A passing privacy and provenance review is required.

Dataset facts

Source
Stanford University
Evidence
secondary claim
Formats
hdf5
hours
40
tasks
6
Source-reported scale
240 trajectories
Read paper

Loop signals

5/7 present or partial

Gaze / attention

No decision-grade evidence captured yet.

unknown
Feedback / correction / failure

No decision-grade evidence captured yet.

unknown
View 5 more signal categories
Observation / ego video

video · HumanPlus is a Stanford full-stack system that lets a customized 33-DoF Unitree H1 humanoid shadow human body and hand motion in real time from RGB cameras, and then learn autonomous whole-body skills via behavior cloning on teleoperated demonstrations. The low-level shadowing policy is trained in simulation using the 40-hour AMASS human-motion dataset; the released task data consists of HDF5 imitation-learning episodes (ACT/Mobile-ALOHA style) recording two head-mounted egocentric RGB cameras plus 19-DoF body and dexterous-hand joint positions. Demonstrated skills include folding clothes, rearranging objects, warehouse unloading, two-robot greeting, wearing a shoe, and typing.

present
Action / hand pose / robot state

HumanPlus is a Stanford full-stack system that lets a customized 33-DoF Unitree H1 humanoid shadow human body and hand motion in real time from RGB cameras, and then learn autonomous whole-body skills via behavior cloning on teleoperated demonstrations. The low-level shadowing policy is trained in simulation using the 40-hour AMASS human-motion dataset; the released task data consists of HDF5 imitation-learning episodes (ACT/Mobile-ALOHA style) recording two head-mounted egocentric RGB cameras plus 19-DoF body and dexterous-hand joint positions. Demonstrated skills include folding clothes, rearranging objects, warehouse unloading, two-robot greeting, wearing a shoe, and typing.

partial
Language intent / task phase

HumanPlus is a Stanford full-stack system that lets a customized 33-DoF Unitree H1 humanoid shadow human body and hand motion in real time from RGB cameras, and then learn autonomous whole-body skills via behavior cloning on teleoperated demonstrations. The low-level shadowing policy is trained in simulation using the 40-hour AMASS human-motion dataset; the released task data consists of HDF5 imitation-learning episodes (ACT/Mobile-ALOHA style) recording two head-mounted egocentric RGB cameras plus 19-DoF body and dexterous-hand joint positions. Demonstrated skills include folding clothes, rearranging objects, warehouse unloading, two-robot greeting, wearing a shoe, and typing.

partial
Sim-real pairing

HumanPlus is a Stanford full-stack system that lets a customized 33-DoF Unitree H1 humanoid shadow human body and hand motion in real time from RGB cameras, and then learn autonomous whole-body skills via behavior cloning on teleoperated demonstrations. The low-level shadowing policy is trained in simulation using the 40-hour AMASS human-motion dataset; the released task data consists of HDF5 imitation-learning episodes (ACT/Mobile-ALOHA style) recording two head-mounted egocentric RGB cameras plus 19-DoF body and dexterous-hand joint positions. Demonstrated skills include folding clothes, rearranging objects, warehouse unloading, two-robot greeting, wearing a shoe, and typing.

present
License / format / access

Open · unknown · hdf5

partial
Evidence details and provenance

Official signal claims

ProprioceptionVideo

Schema and annotations

No machine-readable schema facts are captured.

Sample verification

Pending. Metadata does not prove sample coverage, alignment, or file integrity.

curated official source

Integration notes

  • Metadata requires review against the official source before publication.