Robotic Interestingness Dataset (SubTF)
The Robotic Interestingness Dataset (also called the SubT Front Camera, or SubTF, dataset) from CMU's AirLab is a visual scene dataset for studying online prediction of visually 'interesting' scenes for mobile robots. It consists of 7 long-form video sequences (~53-83 minutes each, ~467.7 minutes / ~7.8 hours total) recorded from the front-facing RGB camera of two fully autonomous unmanned ground vehicles (UGVs) operated by CMU Team Explorer, which won first place at the DARPA Subterranean (SubT) Challenge Tunnel Circuit. The robots explored two underground mine tunnels (cumulative 4-8 km linear distance) under challenging GPS- and communication-denied conditions with poor lighting, dripping water, smoke, and cluttered/irregular geometry. Frames are annotated by multiple human labelers for visual interestingness: 15.49% of frames were marked interesting by at least one annotator and 3.58% by at least two. The data is distributed both as processed image sequences (organized as date-ugvN-tunnelM folders, e.g. 0817-ugv0-tunnel0, with ground-truth and train splits) and as raw ROS bag files, hosted via OneDrive/SharePoint links reachable from the AirLab dataset instructions page. The dataset accompanies the paper 'Visual Memorability for Robotic Interestingness via Unsupervised Online Learning' (ECCV 2020 Oral; extended in IEEE Transactions on Robotics 2021), and is supported by the sair-lab/interestingness code (a three-stage long-term/short-term/online learning visual-memory approach) and the wang-chen/SubT data tools (BSD-3-Clause).
- Scale
- 7.8 hours
- Formats
- custom · rosbag
- License
- unknown
- Published
- 2020-05-18
Decision summary
passive_log
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
70/100
Provisional · confidence 35 · 2/6 evaluated
Access and governance
Access and license are declared by the source.
Schema and signal coverage
No machine-readable schema has been verified yet.
Policy training readiness
Observation/action alignment has not been established.
View 3 more dimensions
World-model readiness
World-model observation, geometry, or temporal semantics are not verified.
Failure and recovery readiness
No verified failure/recovery annotation evidence is available yet.
Download and processing readiness
Scale is declared; transfer and processing estimates are not measured.
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.
Record specifics
Dataset facts
- Source
- CMU AirLab
- Evidence
- secondary claim
- Formats
- custom · rosbag
- episodes
- 7
- hours
- 7.8
Metadata coverage
Loop signals
5/7 present or partial
No decision-grade evidence captured yet.
unknownNo decision-grade evidence captured yet.
unknownView 5 more signal categories
video · The Robotic Interestingness Dataset (also called the SubT Front Camera, or SubTF, dataset) from CMU's AirLab is a visual scene dataset for studying online prediction of visually 'interesting' scenes for mobile robots. It consists of 7 long-form video sequences (~53-83 minutes each, ~467.7 minutes / ~7.8 hours total) recorded from the front-facing RGB camera of two fully autonomous unmanned ground vehicles (UGVs) operated by CMU Team Explorer, which won first place at the DARPA Subterranean (SubT) Challenge Tunnel Circuit. The robots explored two underground mine tunnels (cumulative 4-8 km linear distance) under challenging GPS- and communication-denied conditions with poor lighting, dripping water, smoke, and cluttered/irregular geometry. Frames are annotated by multiple human labelers for visual interestingness: 15.49% of frames were marked interesting by at least one annotator and 3.58% by at least two. The data is distributed both as processed image sequences (organized as date-ugvN-tunnelM folders, e.g. 0817-ugv0-tunnel0, with ground-truth and train splits) and as raw ROS bag files, hosted via OneDrive/SharePoint links reachable from the AirLab dataset instructions page. The dataset accompanies the paper 'Visual Memorability for Robotic Interestingness via Unsupervised Online Learning' (ECCV 2020 Oral; extended in IEEE Transactions on Robotics 2021), and is supported by the sair-lab/interestingness code (a three-stage long-term/short-term/online learning visual-memory approach) and the wang-chen/SubT data tools (BSD-3-Clause).
presentThe Robotic Interestingness Dataset (also called the SubT Front Camera, or SubTF, dataset) from CMU's AirLab is a visual scene dataset for studying online prediction of visually 'interesting' scenes for mobile robots. It consists of 7 long-form video sequences (~53-83 minutes each, ~467.7 minutes / ~7.8 hours total) recorded from the front-facing RGB camera of two fully autonomous unmanned ground vehicles (UGVs) operated by CMU Team Explorer, which won first place at the DARPA Subterranean (SubT) Challenge Tunnel Circuit. The robots explored two underground mine tunnels (cumulative 4-8 km linear distance) under challenging GPS- and communication-denied conditions with poor lighting, dripping water, smoke, and cluttered/irregular geometry. Frames are annotated by multiple human labelers for visual interestingness: 15.49% of frames were marked interesting by at least one annotator and 3.58% by at least two. The data is distributed both as processed image sequences (organized as date-ugvN-tunnelM folders, e.g. 0817-ugv0-tunnel0, with ground-truth and train splits) and as raw ROS bag files, hosted via OneDrive/SharePoint links reachable from the AirLab dataset instructions page. The dataset accompanies the paper 'Visual Memorability for Robotic Interestingness via Unsupervised Online Learning' (ECCV 2020 Oral; extended in IEEE Transactions on Robotics 2021), and is supported by the sair-lab/interestingness code (a three-stage long-term/short-term/online learning visual-memory approach) and the wang-chen/SubT data tools (BSD-3-Clause).
presenthas-success-labels · The Robotic Interestingness Dataset (also called the SubT Front Camera, or SubTF, dataset) from CMU's AirLab is a visual scene dataset for studying online prediction of visually 'interesting' scenes for mobile robots. It consists of 7 long-form video sequences (~53-83 minutes each, ~467.7 minutes / ~7.8 hours total) recorded from the front-facing RGB camera of two fully autonomous unmanned ground vehicles (UGVs) operated by CMU Team Explorer, which won first place at the DARPA Subterranean (SubT) Challenge Tunnel Circuit. The robots explored two underground mine tunnels (cumulative 4-8 km linear distance) under challenging GPS- and communication-denied conditions with poor lighting, dripping water, smoke, and cluttered/irregular geometry. Frames are annotated by multiple human labelers for visual interestingness: 15.49% of frames were marked interesting by at least one annotator and 3.58% by at least two. The data is distributed both as processed image sequences (organized as date-ugvN-tunnelM folders, e.g. 0817-ugv0-tunnel0, with ground-truth and train splits) and as raw ROS bag files, hosted via OneDrive/SharePoint links reachable from the AirLab dataset instructions page. The dataset accompanies the paper 'Visual Memorability for Robotic Interestingness via Unsupervised Online Learning' (ECCV 2020 Oral; extended in IEEE Transactions on Robotics 2021), and is supported by the sair-lab/interestingness code (a three-stage long-term/short-term/online learning visual-memory approach) and the wang-chen/SubT data tools (BSD-3-Clause).
presentThe Robotic Interestingness Dataset (also called the SubT Front Camera, or SubTF, dataset) from CMU's AirLab is a visual scene dataset for studying online prediction of visually 'interesting' scenes for mobile robots. It consists of 7 long-form video sequences (~53-83 minutes each, ~467.7 minutes / ~7.8 hours total) recorded from the front-facing RGB camera of two fully autonomous unmanned ground vehicles (UGVs) operated by CMU Team Explorer, which won first place at the DARPA Subterranean (SubT) Challenge Tunnel Circuit. The robots explored two underground mine tunnels (cumulative 4-8 km linear distance) under challenging GPS- and communication-denied conditions with poor lighting, dripping water, smoke, and cluttered/irregular geometry. Frames are annotated by multiple human labelers for visual interestingness: 15.49% of frames were marked interesting by at least one annotator and 3.58% by at least two. The data is distributed both as processed image sequences (organized as date-ugvN-tunnelM folders, e.g. 0817-ugv0-tunnel0, with ground-truth and train splits) and as raw ROS bag files, hosted via OneDrive/SharePoint links reachable from the AirLab dataset instructions page. The dataset accompanies the paper 'Visual Memorability for Robotic Interestingness via Unsupervised Online Learning' (ECCV 2020 Oral; extended in IEEE Transactions on Robotics 2021), and is supported by the sair-lab/interestingness code (a three-stage long-term/short-term/online learning visual-memory approach) and the wang-chen/SubT data tools (BSD-3-Clause).
partialOpen · unknown · custom · rosbag
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 official source
Integration notes
- Metadata requires review against the official source before publication.
Catalog links