bluerov_pool_data / README.md
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license: apache-2.0

nyu center for robotics & embodied intelligence
lab for autonomous and intelligent robotics
project: embodied at sea (behavioral cloning for marine archaeology & conservation)
robot: blue robotics's "bluerov2"
contributors: Dr. Chris Clark (PI), Greta Perez-Haiek (Lead), Luca Macesanu, Kevin Angers, Timothy Gambin (Advisor)
environment: rectangular swimming pool at NYU CREO, filled with chlorinated water at about a little over a foot's depth of water.
factors: neutral background (shades of blue hexagons with white rimming), neutral lighting (well-lit and constant), disturbances & distractors (variable) Snapshot date: August 18, 2026

This folder contains 8 LeRobot-format (codebase_version v3.0) datasets recorded on a BlueROV2 performing rod-grabbing tasks in a pool, with and without simulated currents. Each dataset is packaged as a .zip containing the standard LeRobot layout (data/, meta/, videos/).

Counts below are taken from each dataset's meta/info.json.

  • Episodes = total_episodes (number of recorded episode/trajectory runs)
  • Frames = total_frames (total timesteps across all episodes, fps = 50.0)
  • Trajectories = total_successes (episodes that ended in a successful grab)
Dataset Episodes Frames Trajectories (successes)
grab green rod 101 26,774 101
grab green rod under currents 110 37,982 101
grab red rod 101 23,550 101
grab red rod under currents 102 30,171 101
grab white rod 102 26,853 100
grab white rod under currents 101 27,934 101
grab yellow rod 101 22,628 101
grab yellow rod under currents 102 28,220 101

Notes

  • "Episodes" counts every recorded run, including any that did not end in a successful grasp; "Trajectories" (total_successes) counts only the successful ones. In most of these datasets nearly all episodes succeeded (only "grab white rod" has 2 non-successful episodes: 102 episodes vs 100 successes).
  • Frame count varies with episode length/duration, not just episode count — e.g. "grab green rod under currents" has the most episodes (110) and by far the most frames (37,982), consistent with currents making the task take longer per episode.
  • robot_type: bluerov2, single task per dataset (total_tasks = 1).