CognitiveDrone Dataset
CognitiveDrone is a simulated UAV vision-language-action dataset from Skoltech's Intelligent Space Robotics Laboratory, built to train drones on tasks that require reasoning rather than pure flight control.
Source notes
CognitiveDrone is a simulated UAV vision-language-action dataset from Skoltech's Intelligent Space Robotics Laboratory, built to train drones on tasks that require reasoning rather than pure flight control. Each trajectory spawns the quadrotor before a set of virtual gates and asks it to fly through the correct one by solving an embedded cognitive task, across three categories: Human Recognition, Symbol Understanding, and Reasoning. Episodes are generated in Gazebo with ArduPilot SITL, recording 256x256 first-person RealSense frames plus 4D velocity commands (Vx, Vy, Vz, ω) at each step.
Two caveats: the release declares no license, and only 30 of the intended 128 RLDS shards were actually uploaded (with no TFDS metadata), so roughly a quarter of the 8,062 trajectories are retrievable from HuggingFace. The 436 complete raw validation episodes in the collector repo are fully usable — the sample frames here come from those.
- Scale
- 2.47 GB size
- Formats
- rlds
- License
- unknown
- Published
- 2025-03-03
Decision summary
scripted
Metadata requires review against the official source before publication.
Read the dataset manifest and feature schema.
Release history
Source-backed release timing for this canonical dataset record.
- Release evidence
Dataset series published
Release timing is recorded from official dataset metadata.
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
73/100
Provisional
4/6 dimensions scored · 48% confidence
Read the evidence behind all 6 dimensions
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.
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
- 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.
Dataset facts
- Source
- Skoltech — Intelligent Space Robotics Laboratory
- Evidence
- secondary claim
- Formats
- rlds
- episodes
- 8,062
- tasks
- 3
- size
- 2.47 GB
Loop signals
6/7 present or partial
No decision-grade evidence captured yet.
unknownView 6 more signal categories
video
presentCognitiveDrone is a simulated UAV vision-language-action dataset from Skoltech's Intelligent Space Robotics Laboratory, built to train drones on tasks that require reasoning rather than pure flight control. Each trajectory spawns the quadrotor before a set of virtual gates and asks it to fly through the correct one by solving an embedded cognitive task, across three categories: Human Recognition, Symbol Understanding, and Reasoning. Episodes are generated in Gazebo with ArduPilot SITL, recording 256x256 first-person RealSense frames plus 4D velocity commands (Vx, Vy, Vz, ω) at each step. *Two caveats: the release declares no license, and only 30 of the intended 128 RLDS shards were actually uploaded (with no TFDS metadata), so roughly a quarter of the 8,062 trajectories are retrievable from HuggingFace. The 436 complete raw validation episodes in the collector repo are fully usable — the sample frames here come from those.*
presentCognitiveDrone is a simulated UAV vision-language-action dataset from Skoltech's Intelligent Space Robotics Laboratory, built to train drones on tasks that require reasoning rather than pure flight control. Each trajectory spawns the quadrotor before a set of virtual gates and asks it to fly through the correct one by solving an embedded cognitive task, across three categories: Human Recognition, Symbol Understanding, and Reasoning. Episodes are generated in Gazebo with ArduPilot SITL, recording 256x256 first-person RealSense frames plus 4D velocity commands (Vx, Vy, Vz, ω) at each step. *Two caveats: the release declares no license, and only 30 of the intended 128 RLDS shards were actually uploaded (with no TFDS metadata), so roughly a quarter of the 8,062 trajectories are retrievable from HuggingFace. The 436 complete raw validation episodes in the collector repo are fully usable — the sample frames here come from those.*
presentlanguage · CognitiveDrone is a simulated UAV vision-language-action dataset from Skoltech's Intelligent Space Robotics Laboratory, built to train drones on tasks that require reasoning rather than pure flight control. Each trajectory spawns the quadrotor before a set of virtual gates and asks it to fly through the correct one by solving an embedded cognitive task, across three categories: Human Recognition, Symbol Understanding, and Reasoning. Episodes are generated in Gazebo with ArduPilot SITL, recording 256x256 first-person RealSense frames plus 4D velocity commands (Vx, Vy, Vz, ω) at each step. *Two caveats: the release declares no license, and only 30 of the intended 128 RLDS shards were actually uploaded (with no TFDS metadata), so roughly a quarter of the 8,062 trajectories are retrievable from HuggingFace. The 436 complete raw validation episodes in the collector repo are fully usable — the sample frames here come from those.*
presentsimulation · CognitiveDrone is a simulated UAV vision-language-action dataset from Skoltech's Intelligent Space Robotics Laboratory, built to train drones on tasks that require reasoning rather than pure flight control. Each trajectory spawns the quadrotor before a set of virtual gates and asks it to fly through the correct one by solving an embedded cognitive task, across three categories: Human Recognition, Symbol Understanding, and Reasoning. Episodes are generated in Gazebo with ArduPilot SITL, recording 256x256 first-person RealSense frames plus 4D velocity commands (Vx, Vy, Vz, ω) at each step. *Two caveats: the release declares no license, and only 30 of the intended 128 RLDS shards were actually uploaded (with no TFDS metadata), so roughly a quarter of the 8,062 trajectories are retrievable from HuggingFace. The 436 complete raw validation episodes in the collector repo are fully usable — the sample frames here come from those.*
presentOpen · unknown · rlds
presentEvidence 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.