DexMimicGen
DexMimicGen is an automated data-generation system that synthesizes large-scale trajectory datasets for bimanual dexterous manipulation from a handful of human teleoperation demonstrations. It generated over 21,000 demonstrations from 60 source human demos across 9 simulation tasks in robosuite/MuJoCo, spanning three embodiments (bimanual Panda arms with parallel-jaw grippers, bimanual Panda arms with dexterous hands, and a Fourier GR-1 humanoid with Inspire dexterous hands), and includes a Real2Sim2Real pipeline validated on a real GR-1 robot.
- Scale
- 21000 episodes
- Formats
- hdf5
- License
- CC-BY-NC-4.0
- Published
- 2024-10-31
Decision summary
scripted
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
63/100
Provisional · confidence 48 · 4/6 evaluated
Access and governance
The declared license restricts commercial use.
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
Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.
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
- NVIDIA Research (with UT Austin and UC San Diego)
- Evidence
- secondary claim
- Formats
- hdf5
- episodes
- 21000
- tasks
- 9
- bytes
- 64317655449
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
depth · video
presentee_pose · DexMimicGen is an automated data-generation system that synthesizes large-scale trajectory datasets for bimanual dexterous manipulation from a handful of human teleoperation demonstrations. It generated over 21,000 demonstrations from 60 source human demos across 9 simulation tasks in robosuite/MuJoCo, spanning three embodiments (bimanual Panda arms with parallel-jaw grippers, bimanual Panda arms with dexterous hands, and a Fourier GR-1 humanoid with Inspire dexterous hands), and includes a Real2Sim2Real pipeline validated on a real GR-1 robot.
presentDexMimicGen is an automated data-generation system that synthesizes large-scale trajectory datasets for bimanual dexterous manipulation from a handful of human teleoperation demonstrations. It generated over 21,000 demonstrations from 60 source human demos across 9 simulation tasks in robosuite/MuJoCo, spanning three embodiments (bimanual Panda arms with parallel-jaw grippers, bimanual Panda arms with dexterous hands, and a Fourier GR-1 humanoid with Inspire dexterous hands), and includes a Real2Sim2Real pipeline validated on a real GR-1 robot.
partialdepth · simulation · DexMimicGen is an automated data-generation system that synthesizes large-scale trajectory datasets for bimanual dexterous manipulation from a handful of human teleoperation demonstrations. It generated over 21,000 demonstrations from 60 source human demos across 9 simulation tasks in robosuite/MuJoCo, spanning three embodiments (bimanual Panda arms with parallel-jaw grippers, bimanual Panda arms with dexterous hands, and a Fourier GR-1 humanoid with Inspire dexterous hands), and includes a Real2Sim2Real pipeline validated on a real GR-1 robot.
presentOpen · CC-BY-NC-4.0 · 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