Datasets:
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action list | observation.state list | observation.environment_state list | timestamp float32 0 23 | frame_index int64 0 1.15k | episode_index int64 0 87 | index int64 0 69.4k | task_index int64 0 0 |
|---|---|---|---|---|---|---|---|
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End of preview. Expand in Data Studio
This dataset was created using LeRobot.
Dataset Structure
{
"codebase_version": "v3.0",
"robot_type": "custom_manipulator",
"total_episodes": 88,
"total_frames": 69450,
"total_tasks": 1,
"chunks_size": 1000,
"data_files_size_in_mb": 20,
"video_files_size_in_mb": 200,
"fps": 50,
"splits": {
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"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"features": {
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},
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"source_file": "/home/lzanetti/workspaces/lerobot_teleop_ws/recording_strategy/charger_insertion_fixed_setup_80train_8val.json",
"strategy": {
"dataset_name": "charger_power_strip_middle_outlet_act_v1",
"version": "1.1",
"task": "From a fixed robot joint configuration, grasp a charger from a fixed pickup pose and insert it into the middle outlet of a fixed power strip, then release and retract.",
"recommended_dataset_size": {
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"validation_successes": 8,
"total_kept_successes": 88,
"practice_runs_not_counted": 10,
"rationale": "The scene, charger pose, target outlet, robot start, and grasp are fixed, so this is a narrow deterministic task. Eighty high-quality training demonstrations should be a strong first round while still covering natural execution variation in grasping and contact. Use targeted additional data only after observing deployment failures.",
"additional_data_rule": "After the first training run, add 20 to 40 targeted demonstrations only for recurring deployment failure modes such as poor grasp, lateral edge contact, incomplete insertion, or failed release."
},
"safety": {
"mandatory": "The power strip must be physically disconnected from mains power during all demonstrations and autonomous rollouts.",
"fixture_rule": "Rigidly constrain the power strip so it cannot translate or rotate when the charger contacts it.",
"stop_conditions": [
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"charger becomes jammed",
"excessive insertion force",
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]
},
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2,
3,
4,
5,
6,
7,
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10,
11,
12,
13,
14,
15,
16,
17,
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"target_outlet": "middle",
"charger_type": "fixed",
"power_strip_type": "fixed",
"camera_mounts": "fixed",
"camera_calibration": "fixed",
"lighting": "approximately fixed",
"control_mode": "fixed"
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"allowed_natural_variation": "Small unavoidable differences in the operator trajectory, grasp settling, and first contact are acceptable as long as the canonical strategy remains consistent.",
"warning": "Do not deliberately create many different approach or insertion strategies from the same visual state; that would make the demonstrations unnecessarily multimodal."
},
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"Start recording before robot motion begins.",
"Approach and grasp the charger using charger_grasp_nominal.",
"Close the gripper fully and verify a stable grasp.",
"Lift without dragging or rotating the charger.",
"Move to a repeatable pre-insertion pose in front of the middle outlet.",
"Align the charger prongs with the outlet.",
"Slow down before first contact.",
"Insert along the outlet axis using controlled force.",
"If a small natural misalignment occurs, back off once and realign; do not repeatedly wiggle.",
"Stop at the predefined full-insertion depth.",
"Open the gripper.",
"Retract without disturbing the inserted charger.",
"Stop recording after retraction is complete."
],
"qa_policy": {
"A": "Smooth successful grasp, transport, alignment, insertion, release, and retraction. Use for training or validation.",
"B": "Successful but contains hesitation, mild scraping, unnecessary correction, or inconsistent speed. Prefer to redo.",
"C": "Failed grasp, regrasp, drop, jam, hard collision, excessive force, wrong outlet, strip motion, or recording fault. Reject.",
"failed_attempt_storage": "Keep failed attempts in a separate diagnostic dataset, but do not mix them into the initial vanilla ACT training set."
},
"collection_schedule": {
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"validation_episodes": "81-82"
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{
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{
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"validation_episodes": "85-86"
},
{
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"validation_episodes": "87-88"
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],
"note": "Interleaving held-out validation recordings across the session helps validation reflect the same minor session drift as the training data. Store them separately and never train on them."
},
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],
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"training_count": 80,
"validation_count": 8,
"total_count": 88,
"training_focus_counts": {
"nominal_complete": 60,
"grasp_consistency": 10,
"contact_consistency": 5,
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"all_initial_conditions_fixed": true,
"target_outlet_varied": false,
"recommended_first_training_round": "Use all 80 successful training episodes and monitor loss on the 8 held-out episodes."
},
"change_log": {
"1.1": "Moved each episode's focus_instruction text into read_aloud and removed the separate focus_instruction field."
}
}
}
}
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