OpenBot
Platform
VLAOpenReadiness 80/100Provisional

LingBot Depth Pretrain Vitl 14 v0.5

robbyant

LingBot-Depth transforms incomplete and noisy depth sensor data into high-quality, metric-accurate 3D measurements. This is the general-purpose pretrained model for depth refinement tasks.

Code
Unverified
Weights
Available
Checkpoint
Available
License
apache-2.0
WeightsPaperModel index A–Z

Decision summary

Best for

navigation

Main blocker

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

Next check

Run a checkpoint loading smoke test before marking the model pipeline-tested.

Release history

Source-backed release timing for this canonical model record.

Important releases

  1. Checkpoint published

    Release timing is recorded from the official source creation date.

    Official source

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 work

80/100

Provisional

3/6 dimensions scored · 43% confidence

Access and governance75conf. 85
Artifact availability90conf. 90
Training and loading reproducibilityNot scoredconf. 15
Training data requirements76conf. 45
Evaluation evidenceNot scoredconf. 10
Deployment readinessNot scoredconf. 10
Engineering checks (OBRS) behind this record
Engineering checksBronze

Needs Audit

5/12checks passed

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

Standardization & Loaders15 / 25 pt
Physical & Action Quality2 / 25 pt
Semantic & Annotation20 / 20 pt
Real-World Validation0 / 15 pt
License & Compliance10 / 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.

Why these scores

The strongest decision reasons behind Selection readiness.

Top 3 adoption signals

Artifact availability

Useful

Checkpoint files were verified in the official repository metadata.

90 · confidence 90

Training and loading reproducibility

Unknown

No verified loading configuration is available.

— · confidence 15

Evaluation evidence

Unknown

No structured evaluation evidence has been verified.

— · confidence 10

Data requirements

Declared loop-data needs, missing evidence, and the next checks that matter.

3 required categories

Observation / ego video

video · depth · depth-estimation · depth-completion

required

Language intent / task phase

language

required

Sim-real / embodiment metadata

depth · depth-estimation · depth-completion

required

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.

Browse datasets

No related catalog datasets are linked to this model yet.

More evidenceShow

Additional decision dimensions

Access and governanceUseful

Access and license are declared by the source.

75 · c85
Training data requirementsUseful

Required signal categories are structured; exact tensor and action interfaces still need verification.

76 · c45
Deployment readinessUnknown

Hardware, latency, dependencies, and checkpoint loading are not pipeline-tested.

— · c10

Artifact facts

availability
metadata_verified
open
repository metadata
license
metadata_verified
apache-2.0
repository metadata: license
repository.files
metadata_verified
.gitattributes · README.md · model.pt
repository file tree
dataset.signals
official_claim
video · depth · language · depth-estimation · depth-completion
README/card declaration
dataset.scale
metadata_verified
label: repository size · value: 1.28GB
repository blob sizes
model.weight_files
metadata_verified
model.pt
repository file tree
model.checkpoint_files
metadata_verified
model.pt
repository file tree