UAV-Flow-Sim
UAV-Flow-Sim is the simulated half of the UAV-Flow benchmark, built in an Unreal Engine campus environment and used for systematic, repeatable evaluation of language-conditioned UAV policies. It contains 10,109 trajectories spanning 10 Flow task types, with the same schema as the real split — atomic language instruction, egocentric RGB observation, and 6-DoF UAV state. Collection was hybrid: human pilots manually flew trajectories by locating landmarks in the simulator, while a rule-based collector exploited the simulator's ground-truth scene structure. The paper gives no breakdown between the two, so both collection modes are tagged. *Kept as its own entry rather than merged into [UAV-Flow](/d/uav-flow): different realness, a ~7x size difference, and a different role (evaluation vs training).*
- Scale
- 10109 episodes
- Formats
- custom
- License
- unknown
- Published
- 2025-05-14
Decision summary
scripted
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Catalog assessment
Selection evidence
73/100
Provisional · confidence 48 · 4/6 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 and action/state signals are declared; alignment quality still depends on sample verification.
View 3 more dimensions
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
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.
Next checks
- Read the dataset manifest and feature schema.
- Run a bounded sample audit before assigning Strong readiness.
Record specifics
Dataset facts
- Source
- Beihang University — Institute of Artificial Intelligence
- Evidence
- secondary claim
- Formats
- custom
- episodes
- 10109
- tasks
- 10
- bytes
- 35925860784
Metadata coverage
Loop signals
6/7 present or partial
No decision-grade evidence captured yet.
unknownView 6 more signal categories
video · UAV-Flow-Sim is the simulated half of the UAV-Flow benchmark, built in an Unreal Engine campus environment and used for systematic, repeatable evaluation of language-conditioned UAV policies. It contains 10,109 trajectories spanning 10 Flow task types, with the same schema as the real split — atomic language instruction, egocentric RGB observation, and 6-DoF UAV state. Collection was hybrid: human pilots manually flew trajectories by locating landmarks in the simulator, while a rule-based collector exploited the simulator's ground-truth scene structure. The paper gives no breakdown between the two, so both collection modes are tagged. *Kept as its own entry rather than merged into [UAV-Flow](/d/uav-flow): different realness, a ~7x size difference, and a different role (evaluation vs training).*
presentUAV-Flow-Sim is the simulated half of the UAV-Flow benchmark, built in an Unreal Engine campus environment and used for systematic, repeatable evaluation of language-conditioned UAV policies. It contains 10,109 trajectories spanning 10 Flow task types, with the same schema as the real split — atomic language instruction, egocentric RGB observation, and 6-DoF UAV state. Collection was hybrid: human pilots manually flew trajectories by locating landmarks in the simulator, while a rule-based collector exploited the simulator's ground-truth scene structure. The paper gives no breakdown between the two, so both collection modes are tagged. *Kept as its own entry rather than merged into [UAV-Flow](/d/uav-flow): different realness, a ~7x size difference, and a different role (evaluation vs training).*
partiallanguage · UAV-Flow-Sim is the simulated half of the UAV-Flow benchmark, built in an Unreal Engine campus environment and used for systematic, repeatable evaluation of language-conditioned UAV policies. It contains 10,109 trajectories spanning 10 Flow task types, with the same schema as the real split — atomic language instruction, egocentric RGB observation, and 6-DoF UAV state. Collection was hybrid: human pilots manually flew trajectories by locating landmarks in the simulator, while a rule-based collector exploited the simulator's ground-truth scene structure. The paper gives no breakdown between the two, so both collection modes are tagged. *Kept as its own entry rather than merged into [UAV-Flow](/d/uav-flow): different realness, a ~7x size difference, and a different role (evaluation vs training).*
presentUAV-Flow-Sim is the simulated half of the UAV-Flow benchmark, built in an Unreal Engine campus environment and used for systematic, repeatable evaluation of language-conditioned UAV policies. It contains 10,109 trajectories spanning 10 Flow task types, with the same schema as the real split — atomic language instruction, egocentric RGB observation, and 6-DoF UAV state. Collection was hybrid: human pilots manually flew trajectories by locating landmarks in the simulator, while a rule-based collector exploited the simulator's ground-truth scene structure. The paper gives no breakdown between the two, so both collection modes are tagged. *Kept as its own entry rather than merged into [UAV-Flow](/d/uav-flow): different realness, a ~7x size difference, and a different role (evaluation vs training).*
presentsimulation · UAV-Flow-Sim is the simulated half of the UAV-Flow benchmark, built in an Unreal Engine campus environment and used for systematic, repeatable evaluation of language-conditioned UAV policies. It contains 10,109 trajectories spanning 10 Flow task types, with the same schema as the real split — atomic language instruction, egocentric RGB observation, and 6-DoF UAV state. Collection was hybrid: human pilots manually flew trajectories by locating landmarks in the simulator, while a rule-based collector exploited the simulator's ground-truth scene structure. The paper gives no breakdown between the two, so both collection modes are tagged. *Kept as its own entry rather than merged into [UAV-Flow](/d/uav-flow): different realness, a ~7x size difference, and a different role (evaluation vs training).*
presentOpen · unknown · custom
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