OpenBot
Manipulation datasetUnknownReadiness 82/100Provisional

mdm_depth

LingBot-Depth Dataset

Source notes

LingBot-Depth Dataset

Self-curated RGB-D dataset for training LingBot-Depth, a masked depth modeling approach (arxiv:2601.17895). Each sample contains an RGB image, raw sensor depth, and ground truth depth.

Total size: 2.71 TBDepth scale: millimeters (mm), stored as 16-bit PNGLicense: CC BY-NC-SA 4.0

	Sub-datasets




	

Name Description Samples

RobbyReal Real-world indoor scenes captured with multiple RGB-D cameras 1,400,000

RobbyVla Real-world data… See the full description on the dataset page: https://huggingface.co/datasets/robbyant/mdm_depth.

Scale
Not declared
Formats
Not declared
License
apache-2.0
Published
2026-03-21
Embodiments
Not declared
Modalities
Not declared

Decision summary

Best for

robotics

Main blocker

No verified blocker is published; unresolved facts remain unknown.

Next check

Read the dataset manifest and feature schema.

Release history

Source-backed release timing for this canonical dataset record.

Important releases
  1. Dataset published

    Release timing is recorded from the official source creation date.

    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

How scores work

82/100

Provisional

1/6 dimensions scored · 21% confidence

Access and governanceNot scoredconf. 20
Schema and signal coverageNot scoredconf. 15
Policy training readinessNot scoredconf. 15
World-model readiness82conf. 50
Failure and recovery readinessNot scoredconf. 15
Download and processing readinessNot scoredconf. 10
Read the evidence behind all 6 dimensions

Access and governance

Access and license are declared by the source.

unknownnot scored · confidence 20

Schema and signal coverage

No machine-readable schema has been verified yet.

unknownnot scored · confidence 15

Policy training readiness

Observation/action alignment has not been established.

unknownnot scored · confidence 15

World-model readiness

Temporal observations plus geometry or semantic context are declared; sample alignment remains to be audited.

usefulfit 82 · confidence 50

Failure and recovery readiness

No verified failure/recovery annotation evidence is available yet.

unknownnot scored · confidence 15

Download and processing readiness

Dataset scale and sampling path are unknown.

unknownnot scored · confidence 10
Review unresolved evidence and next checks

Signal gaps

  • Action / hand pose / robot state. Not enough evidence is available to classify this signal.
  • 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.
Engineering checks (OBRS) behind this record
Engineering checksBronze

Needs Audit

3/12checks passed

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

Standardization & Loaders2 / 25 pt
Physical & Action Quality2 / 25 pt
Semantic & Annotation17 / 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.

Dataset facts

Source
robbyant
Evidence
official claim
Formats
Not declared
Read paper

Loop signals

4/7 present or partial

Action / hand pose / robot state

No decision-grade evidence captured yet.

unknown
Gaze / attention

No decision-grade evidence captured yet.

unknown
View 5 more signal categories
Observation / ego video

video · depth · task_categories:depth-estimation · LingBot-Depth Dataset Self-curated RGB-D dataset for training LingBot-Depth, a masked depth modeling approach (arxiv:2601.17895). Each sample contains an RGB image, raw sensor depth, and ground truth depth. Total size: 2.71 TBDepth scale: millimeters (mm), stored as 16-bit PNGLicense: CC BY-NC-SA 4.0 Sub-datasets Name Description Samples RobbyReal Real-world indoor scenes captured with multiple RGB-D cameras 1,400,000 RobbyVla Real-world data… See the full description on the dataset page: https://huggingface.co/datasets/robbyant/mdm_depth.

present
Language intent / task phase

language · task_categories:depth-estimation · language:en

present
Feedback / correction / failure

No decision-grade evidence captured yet.

unknown
Sim-real pairing

depth · task_categories:depth-estimation · LingBot-Depth Dataset Self-curated RGB-D dataset for training LingBot-Depth, a masked depth modeling approach (arxiv:2601.17895). Each sample contains an RGB image, raw sensor depth, and ground truth depth. Total size: 2.71 TBDepth scale: millimeters (mm), stored as 16-bit PNGLicense: CC BY-NC-SA 4.0 Sub-datasets Name Description Samples RobbyReal Real-world indoor scenes captured with multiple RGB-D cameras 1,400,000 RobbyVla Real-world data… See the full description on the dataset page: https://huggingface.co/datasets/robbyant/mdm_depth.

present
License / format / access

Unknown · apache-2.0

present
Evidence details and provenance

Official signal claims

VideoDepthLanguageTask_categories:depth EstimationLanguage:en

Schema and annotations

No machine-readable schema facts are captured.

Sample verification

Pending. Metadata does not prove sample coverage, alignment, or file integrity.

README/card declaration

Integration notes

No integration notes are published.