LingBot Masked Depth Modeling Data
A roughly three-million-sample mixture of real indoor, robot-manipulation, and simulated RGB-D data used by LingBot-Depth.
- Scale
- 3.0M samples
- Formats
- RGB-D · Camera intrinsics
- License
- Not declared
- Published
- 2026-03-21
- Embodiments
- RGB-D capture rigs, VLA robot manipulation subset
- Modalities
- images, depth, camera calibration +2
Decision summary
Depth completion
Perception dataset rather than an action-policy training corpus; RobbyVla is the manipulation-related subset.
Verify repository and artifact licenses.
Release history
Source-backed release timing for this canonical dataset record.
- Release evidence
Dataset series published
Release timing is recorded from the official source creation date.
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 work82/100
Provisional
2/6 dimensions scored · 30% confidence
Read the evidence behind all 6 dimensions
Access and governance
License or access terms are not verified.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation/action alignment has not been established.
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
A bounded sample/subset is declared for initial validation.
Review unresolved evidence and next checks
Signal gaps
- Gaze / attention. Not enough evidence is available to classify this signal.
- Language intent / task phase. Not enough evidence is available to classify this signal.
- Feedback / correction / failure. Not enough evidence is available to classify this signal.
Next checks
- Verify repository and artifact licenses.
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
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.
Dataset facts
- Source
- Robbyant
- Evidence
- official claim
- Formats
- RGB-D · Camera intrinsics · Hugging Face dataset
- samples
- 3.0M
- real
- 2.0M
- simulated
- 1.0M
Loop signals
4/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
images · depth · camera calibration · RGB-D
presentPerception dataset rather than an action-policy training corpus; RobbyVla is the manipulation-related subset. · A roughly three-million-sample mixture of real indoor, robot-manipulation, and simulated RGB-D data used by LingBot-Depth.
partialNo decision-grade evidence captured yet.
unknowndepth · camera calibration · sim-real · Depth completion
presentOpen · RGB-D · Camera intrinsics
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 source metadata
Integration notes
- Perception dataset rather than an action-policy training corpus; RobbyVla is the manipulation-related subset.