OpenBot
Platform
Manipulation datasetOpen
OBRS 38•Bronze

MimicGen

MimicGen is a data-generation system (CoRL 2023, NVIDIA + UT Austin) that synthesizes large-scale robot manipulation datasets by adapting a small set of human teleoperation demonstrations to new object poses, objects,…

Source notes

MimicGen is a data-generation system (CoRL 2023, NVIDIA + UT Austin) that synthesizes large-scale robot manipulation datasets by adapting a small set of human teleoperation demonstrations to new object poses, objects, and robot embodiments. The public release contains over 48,000 simulated task demonstrations across 12 tasks (categories: source/core/object/robot/large_interpolation) in robosuite/MuJoCo, stored as robomimic-compatible HDF5 with low-dim and image observations. The broader system generated 50K+ demos across 18 tasks over two simulators (robosuite, Isaac Gym) and a physical robot arm.

Scale
160 GB size
Formats
hdf5
License
CC-BY-4.0
Published
2023-10-26
Official sourceAll recordsDataset index A–ZRun local preflightRequest scoped audit

Decision summary

Best for

scripted

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

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.

Dataset facts

Source
NVIDIA (with The University of Texas at Austin)
Evidence
secondary claim
Formats
hdf5
episodes
48K
tasks
12
size
160 GB
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 · MimicGen is a data-generation system (CoRL 2023, NVIDIA + UT Austin) that synthesizes large-scale robot manipulation datasets by adapting a small set of human teleoperation demonstrations to new object poses, objects, and robot embodiments. The public release contains over 48,000 simulated task demonstrations across 12 tasks (categories: source/core/object/robot/large_interpolation) in robosuite/MuJoCo, stored as robomimic-compatible HDF5 with low-dim and image observations. The broader system generated 50K+ demos across 18 tasks over two simulators (robosuite, Isaac Gym) and a physical robot arm.

present
Action / hand pose / robot state

ee_pose · MimicGen is a data-generation system (CoRL 2023, NVIDIA + UT Austin) that synthesizes large-scale robot manipulation datasets by adapting a small set of human teleoperation demonstrations to new object poses, objects, and robot embodiments. The public release contains over 48,000 simulated task demonstrations across 12 tasks (categories: source/core/object/robot/large_interpolation) in robosuite/MuJoCo, stored as robomimic-compatible HDF5 with low-dim and image observations. The broader system generated 50K+ demos across 18 tasks over two simulators (robosuite, Isaac Gym) and a physical robot arm.

partial
Language intent / task phase

MimicGen is a data-generation system (CoRL 2023, NVIDIA + UT Austin) that synthesizes large-scale robot manipulation datasets by adapting a small set of human teleoperation demonstrations to new object poses, objects, and robot embodiments. The public release contains over 48,000 simulated task demonstrations across 12 tasks (categories: source/core/object/robot/large_interpolation) in robosuite/MuJoCo, stored as robomimic-compatible HDF5 with low-dim and image observations. The broader system generated 50K+ demos across 18 tasks over two simulators (robosuite, Isaac Gym) and a physical robot arm.

partial
Sim-real pairing

simulation · MimicGen is a data-generation system (CoRL 2023, NVIDIA + UT Austin) that synthesizes large-scale robot manipulation datasets by adapting a small set of human teleoperation demonstrations to new object poses, objects, and robot embodiments. The public release contains over 48,000 simulated task demonstrations across 12 tasks (categories: source/core/object/robot/large_interpolation) in robosuite/MuJoCo, stored as robomimic-compatible HDF5 with low-dim and image observations. The broader system generated 50K+ demos across 18 tasks over two simulators (robosuite, Isaac Gym) and a physical robot arm.

present
License / format / access

Open · CC-BY-4.0 · hdf5

partial
Evidence details and provenance

Official signal claims

Ee_poseProprioceptionVideo

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.