Diffusion Policy
Columbia / Toyota Research Institute / collaborators
Visuomotor policy approach that represents robot behavior as a conditional denoising diffusion process.
- Code
- Not listed
- Weights
- Available
- Checkpoint
- Available
- License
- apache-2.0
Decision summary
Multi-modal action generation
Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.
Run a checkpoint loading smoke test before marking the model pipeline-tested.
Release history
Source-backed release timing for this canonical model record.
- Paper
Model series introduced
Release timing is anchored to the cited paper publication date.
Selection readiness
Evidence dimensions for deciding whether this model is ready to inspect, compare, or adopt. Scores describe catalog evidence, not task performance.
Published evaluation
How scores work76/100
Useful
5/6 dimensions scored · 63% confidence
Engineering checks (OBRS) behind this record
Needs Audit
OBRS metadata and reported test evidence. A breakdown behind Selection readiness, not a separate score or an independent OpenBot certification.
- A passing real-hardware test report is required.
- A passing ingestion/pipeline test report is required.
- A passing privacy and provenance review is required.
Why these scores
The strongest decision reasons behind Selection readiness.
Artifact availability
UsefulCheckpoint files were verified in the official repository metadata.
Training and loading reproducibility
UsefulConfiguration files are present; runtime loading is not tested.
Evaluation evidence
UnknownNo structured evaluation evidence has been verified.
Data requirements
Declared loop-data needs, missing evidence, and the next checks that matter.
Observation / ego video
observation
Action / robot state
actions · robot state · trajectory
Feedback / correction / failure
task success
Sim-real / embodiment metadata
robot state
Critical gaps
Core categories are represented. Interface alignment and data quality still require verification.
Linked datasets
Signal-level links only. Verify runtime interfaces before use.
EgoWorld
Schema validation for LeRobot v3
provisional fit 83 · confidence 40
2 matched · 1 unknown
EgoStation GoPro Pick-and-Place
Pick-and-place benchmark fixtures
provisional fit 83 · confidence 40
2 matched · 1 unknown
Egocentric Adjust Bottle
LeRobot conversion tests
provisional fit 83 · confidence 40
2 matched · 1 unknown
More evidenceShowHide
Additional decision dimensions
Access and license are declared by the source.
75 · c85Required signal categories are structured; exact tensor and action interfaces still need verification.
76 · c70Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.
70 · c55Artifact facts
- release_year
- official_claim
- 2023
- curated official source year
- model.required_signals
- official_claim
- observation · actions · robot state · trajectory · task success
- Official model documentation and curated signal mapping
- model.hub
- official_claim
- declared
- curated model hub link
- model.weight_files
- metadata_verified
- repository: lerobot/diffusion_pusht · count: 1 · sample files: model.safetensors
- Hugging Face model metadata siblings
- model.checkpoint_files
- metadata_verified
- repository: lerobot/diffusion_pusht · count: 1 · sample files: model.safetensors
- Hugging Face model metadata checkpoint files
- model.config_files
- metadata_verified
- repository: lerobot/diffusion_pusht · count: 1 · sample files: config.json
- Hugging Face model metadata siblings
- license.weights
- metadata_verified
- apache-2.0
- Hugging Face model card license metadata
- license
- metadata_verified
- apache-2.0
- Hugging Face model card license metadata
- release_timing
- metadata_verified
- 2023-03-07T18:50:03.000Z
- arXiv 2303.04137 published
Additional references
OpenBot notes
- Important policy baseline for comparing whether a dataset needs a larger VLA/WAM or a stronger task policy.
- OpenBot failure mining should identify when diffusion policies fail from missing feedback or contact signals.