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 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
Decision summary
teleoperation
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
70/100
Provisional · confidence 42 · 3/6 evaluated
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation and action/state signals are declared; alignment quality still depends on sample verification.
View 3 more dimensions
World-model readiness
World-model observation, geometry, or temporal semantics are not verified.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Scale is declared; transfer and processing estimates are not measured.
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.
Record specifics
Dataset facts
- Source
- Stanford University
- Evidence
- secondary claim
- Formats
- hdf5
- hours
- 40
- tasks
- 6
Metadata coverage
Loop signals
5/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
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.
presentHumanPlus 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.
partialHumanPlus 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.
partialHumanPlus 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.
presentOpen · unknown · hdf5
partialEvidence details and provenance
Official signal claims
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.
Catalog links