carrot_in_pot_raw / dataset_stats.json
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{
"dataset": "Bigenlight/carrot_in_pot_raw",
"generated_utc": "2026-09-14T14:28:17+00:00",
"generated_by": "make_carrot_raw_stats.py \u2014 h5py + cv2 + ffprobe audit over all 54 take folders (vectors.h5, depth.h5, cam1.mp4, cam2.mp4). mean rate = (N-1)/(t_last-t_first) per take, then median across takes; dt percentiles pooled over all takes. Video frame counts from `ffprobe -count_frames`. Value ranges pooled over all takes. Depth quality measured on ONE sampled mid frame per camera per take (decoded with cv2.imdecode(..., IMREAD_UNCHANGED)).",
"aggregate": {
"n_takes": 54,
"total_duration_s": 619.33,
"total_cam1_frames": 18557,
"total_cam2_frames": 18557,
"total_depth_cam1_frames": 18541,
"total_depth_cam2_frames": 18538,
"total_bytes": 3729216578,
"total_bytes_cam1_mp4": 185439561,
"total_bytes_cam2_mp4": 218768745,
"total_bytes_vectors_h5": 38692480,
"total_bytes_depth_h5": 3286315792,
"duration_median_s": 10.86,
"duration_min_s": 8.7,
"duration_max_s": 17.78,
"duration_min_take": "take_09_20260914_165429",
"duration_max_take": "take_49_20260914_171812",
"nan_or_inf_hits": [],
"absolute_path_hits": [],
"stray_files": [],
"video_frame_count_method": "ffprobe -count_frames",
"all_video_frame_counts_match_h5": true,
"video_formats": [
"mpeg4|1280|720|yuv420p|30/1"
],
"takes_with_cam1_cam2_diff": {
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"take_03_20260914_164930": 1,
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"take_11_20260914_165557": 1,
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"take_44_20260914_171505": -1,
"take_54_20260914_172116": 1,
"take_55_20260914_172155": -1
},
"takes_with_depth_cam1_colour_diff": {
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"take_05_20260914_165156": 1,
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"take_10_20260914_165510": -1,
"take_11_20260914_165557": -1,
"take_17_20260914_165859": 1,
"take_20_20260914_170107": 1,
"take_21_20260914_170135": -1,
"take_23_20260914_170302": -1,
"take_26_20260914_170447": -1,
"take_27_20260914_170519": -1,
"take_28_20260914_170557": -1,
"take_32_20260914_170803": -1,
"take_34_20260914_170932": -1,
"take_35_20260914_171001": -1,
"take_36_20260914_171037": -1,
"take_38_20260914_171144": -1,
"take_40_20260914_171256": -1,
"take_43_20260914_171435": -1,
"take_44_20260914_171505": 1,
"take_48_20260914_171732": -1,
"take_52_20260914_171957": -1,
"take_54_20260914_172116": -1
},
"takes_with_depth_cam2_colour_diff": {
"take_01_20260914_164811": -1,
"take_02_20260914_164845": -1,
"take_04_20260914_165011": 1,
"take_05_20260914_165156": -1,
"take_07_20260914_165316": -1,
"take_09_20260914_165429": 1,
"take_11_20260914_165557": 1,
"take_14_20260914_165719": -1,
"take_16_20260914_165825": -1,
"take_17_20260914_165859": -1,
"take_18_20260914_165926": -1,
"take_19_20260914_170024": -1,
"take_20_20260914_170107": -1,
"take_21_20260914_170135": 1,
"take_22_20260914_170211": -1,
"take_26_20260914_170447": -1,
"take_28_20260914_170557": -2,
"take_29_20260914_170629": 1,
"take_31_20260914_170732": -1,
"take_33_20260914_170854": -1,
"take_39_20260914_171227": -1,
"take_41_20260914_171328": -1,
"take_43_20260914_171435": -1,
"take_44_20260914_171505": -1,
"take_46_20260914_171603": -1,
"take_48_20260914_171732": -1,
"take_49_20260914_171812": -1,
"take_51_20260914_171924": -1,
"take_54_20260914_172116": 1,
"take_55_20260914_172155": -1
},
"columns_attr_python_types": [
"str"
],
"identical_group_set_across_takes": true,
"groups": [
"cam1_frames",
"cam2_frames",
"command",
"gello_joint_states",
"gripper",
"synchronized",
"tcp_pose",
"ur_joint_states",
"wrench"
],
"takes_with_multiple_grip_closures": [
"take_15_20260914_165753",
"take_39_20260914_171227",
"take_40_20260914_171256"
],
"depth_valid_pct_cam1": {
"min": 85.15,
"median": 86.94,
"max": 88.71
},
"depth_valid_pct_cam2": {
"min": 50.48,
"median": 62.32,
"max": 67.95
},
"depth_median_range_m_cam1": {
"min": 0.526,
"median": 0.54,
"max": 0.549
},
"depth_median_range_m_cam2": {
"min": 0.184,
"median": 0.207,
"max": 0.341
},
"depth_camera_info_identical_across_takes": {
"cam1": true,
"cam2": true
},
"depth_camera_info_mismatch_takes": {
"cam1": [],
"cam2": []
},
"depth_frames_sampled": 216,
"depth_max_mm_sampled": 9899,
"depth_pixels_over_10000mm_sampled": 0,
"depth_valid_pct_at_grasp_cam1": {
"min": 85.36,
"median": 86.91,
"max": 88.35
},
"depth_valid_pct_at_grasp_cam2": {
"min": 37.13,
"median": 61.51,
"max": 67.5
}
},
"rates": {
"cam1_frames": {
"mean_rate_hz_median_over_takes": 30.0,
"mean_rate_hz_min": 29.94,
"mean_rate_hz_max": 30.03,
"median_dt_ms": 33.0,
"p05_dt_ms": 30.0,
"p95_dt_ms": 37.5,
"max_dt_ms": 55.1,
"rate_from_median_dt_hz": 30.3
},
"cam2_frames": {
"mean_rate_hz_median_over_takes": 30.0,
"mean_rate_hz_min": 29.96,
"mean_rate_hz_max": 30.09,
"median_dt_ms": 33.1,
"p05_dt_ms": 30.0,
"p95_dt_ms": 37.5,
"max_dt_ms": 61.0,
"rate_from_median_dt_hz": 30.21
},
"command": {
"mean_rate_hz_median_over_takes": 67.33,
"mean_rate_hz_min": 41.22,
"mean_rate_hz_max": 70.37,
"median_dt_ms": 15.9,
"p05_dt_ms": 4.3,
"p95_dt_ms": 19.1,
"max_dt_ms": 44.1,
"rate_from_median_dt_hz": 62.89
},
"ur_joint_states": {
"mean_rate_hz_median_over_takes": 67.34,
"mean_rate_hz_min": 41.15,
"mean_rate_hz_max": 70.38,
"median_dt_ms": 15.9,
"p05_dt_ms": 4.3,
"p95_dt_ms": 19.1,
"max_dt_ms": 43.3,
"rate_from_median_dt_hz": 62.89
},
"tcp_pose": {
"mean_rate_hz_median_over_takes": 67.34,
"mean_rate_hz_min": 41.16,
"mean_rate_hz_max": 70.34,
"median_dt_ms": 15.9,
"p05_dt_ms": 4.2,
"p95_dt_ms": 19.1,
"max_dt_ms": 40.2,
"rate_from_median_dt_hz": 62.89
},
"wrench": {
"mean_rate_hz_median_over_takes": 67.34,
"mean_rate_hz_min": 41.16,
"mean_rate_hz_max": 70.34,
"median_dt_ms": 15.9,
"p05_dt_ms": 4.2,
"p95_dt_ms": 19.0,
"max_dt_ms": 41.0,
"rate_from_median_dt_hz": 62.89
},
"gripper": {
"mean_rate_hz_median_over_takes": 38.83,
"mean_rate_hz_min": 37.58,
"mean_rate_hz_max": 41.14,
"median_dt_ms": 31.4,
"p05_dt_ms": 2.2,
"p95_dt_ms": 38.1,
"max_dt_ms": 69.4,
"rate_from_median_dt_hz": 31.85
},
"gello_joint_states": {
"mean_rate_hz_median_over_takes": 30.01,
"mean_rate_hz_min": 29.96,
"mean_rate_hz_max": 30.06,
"median_dt_ms": 33.1,
"p05_dt_ms": 20.7,
"p95_dt_ms": 46.1,
"max_dt_ms": 69.6,
"rate_from_median_dt_hz": 30.21
},
"depth_cam1": {
"mean_rate_hz_median_over_takes": 30.0,
"mean_rate_hz_min": 29.92,
"mean_rate_hz_max": 30.07,
"median_dt_ms": 32.97,
"p05_dt_ms": 20.2,
"p95_dt_ms": 47.1,
"max_dt_ms": 68.81,
"rate_from_median_dt_hz": 30.33
},
"depth_cam2": {
"mean_rate_hz_median_over_takes": 30.0,
"mean_rate_hz_min": 29.92,
"mean_rate_hz_max": 30.1,
"median_dt_ms": 32.93,
"p05_dt_ms": 19.86,
"p95_dt_ms": 47.65,
"max_dt_ms": 69.24,
"rate_from_median_dt_hz": 30.37
}
},
"leader_vs_follower": {
"note": "gello_* is the GELLO LEADER. Measured here: the leader joints in this session are NOT a fixed-offset mirror of the follower (contrast cube_in_cup_raw, where joints 1-5 tracked within +/-0.011 rad). Per-joint difference statistics are given for completeness; the per-take median of the difference itself scatters (see *_take_median_std), so there is no single offset to subtract. WHY: this session was recorded in END-EFFECTOR (EEF) delta teleop mode (ur7e_gello_real.launch.py robot_ip:=<ROBOT_IP> headless_mode:=true control_mode:=eef), not joint mode. The bridge computes the leader end-effector pose by forward kinematics of a virtual leader chain, takes the pose delta, and solves IK on the UR7e to produce the joint targets in `command`. The leader's own joint angles are a different kinematic solution, so joint-1 anti-correlation is expected and joint 6 is not a 2*pi wrap. cube_in_cup_raw / banana_in_pot_raw were joint-mode recordings, hence their ~0 offsets. `command` remains the true absolute joint target in both modes.",
"per_joint": {
"gello_q1_minus_ur_q1_rad": {
"median": 2.7019,
"mean": 2.7855,
"p05": 2.1488,
"p95": 3.5632,
"min": 1.9188,
"max": 4.2352,
"take_median_min": 2.367,
"take_median_max": 3.0142,
"take_median_std": 0.1597,
"corr_gello_vs_ur_mean_over_takes": -0.785,
"corr_gello_vs_cmd_mean_over_takes": -0.956,
"corr_dgello_vs_dcmd_mean_over_takes": -0.599,
"n": 18573
},
"gello_q2_minus_ur_q2_rad": {
"median": -0.0147,
"mean": -0.0262,
"p05": -0.5078,
"p95": 0.3989,
"min": -0.7868,
"max": 0.7239,
"take_median_min": -0.5155,
"take_median_max": 0.4012,
"take_median_std": 0.2033,
"corr_gello_vs_ur_mean_over_takes": 0.078,
"corr_gello_vs_cmd_mean_over_takes": 0.261,
"corr_dgello_vs_dcmd_mean_over_takes": 0.22,
"n": 18573
},
"gello_q3_minus_ur_q3_rad": {
"median": -0.0242,
"mean": -0.0225,
"p05": -0.5309,
"p95": 0.5004,
"min": -1.2279,
"max": 0.7814,
"take_median_min": -0.4713,
"take_median_max": 0.5307,
"take_median_std": 0.225,
"corr_gello_vs_ur_mean_over_takes": -0.011,
"corr_gello_vs_cmd_mean_over_takes": -0.059,
"corr_dgello_vs_dcmd_mean_over_takes": 0.084,
"n": 18573
},
"gello_q4_minus_ur_q4_rad": {
"median": -0.3328,
"mean": -0.3129,
"p05": -0.6954,
"p95": 0.1192,
"min": -1.0827,
"max": 0.7897,
"take_median_min": -0.6042,
"take_median_max": 0.1078,
"take_median_std": 0.1669,
"corr_gello_vs_ur_mean_over_takes": 0.537,
"corr_gello_vs_cmd_mean_over_takes": 0.603,
"corr_dgello_vs_dcmd_mean_over_takes": 0.436,
"n": 18573
},
"gello_q5_minus_ur_q5_rad": {
"median": -0.0247,
"mean": -0.0127,
"p05": -0.3031,
"p95": 0.3291,
"min": -0.5478,
"max": 0.5133,
"take_median_min": -0.3536,
"take_median_max": 0.3802,
"take_median_std": 0.1317,
"corr_gello_vs_ur_mean_over_takes": 0.583,
"corr_gello_vs_cmd_mean_over_takes": 0.435,
"corr_dgello_vs_dcmd_mean_over_takes": 0.14,
"n": 18573
},
"gello_q6_minus_ur_q6_rad": {
"median": 2.8886,
"mean": 2.9601,
"p05": 2.2481,
"p95": 3.7253,
"min": 1.8463,
"max": 4.3063,
"take_median_min": 2.4143,
"take_median_max": 3.3033,
"take_median_std": 0.2288,
"corr_gello_vs_ur_mean_over_takes": 0.025,
"corr_gello_vs_cmd_mean_over_takes": -0.031,
"corr_dgello_vs_dcmd_mean_over_takes": 0.166,
"n": 18573
}
}
},
"command_tracking": {
"note": "|command cmd_i - ur_joint_states q_i| after nearest-timestamp alignment of the follower onto the command clock. Measured on the RAW (uncorrected) timestamps, so it is mostly the ~0.9 s timestamp artefact documented in `timestamp_lag`, NOT servo following error: on the corrected clock the 6-joint mean residual falls to 0.0007-0.0103 rad. Pooled over all takes.",
"per_joint": {
"j1": {
"median_of_take_medians_rad": 0.0227,
"mean_rad": 0.050161,
"max_rad": 0.281796
},
"j2": {
"median_of_take_medians_rad": 0.0611,
"mean_rad": 0.065053,
"max_rad": 0.28006
},
"j3": {
"median_of_take_medians_rad": 0.0326,
"mean_rad": 0.043223,
"max_rad": 0.341007
},
"j4": {
"median_of_take_medians_rad": 0.0796,
"mean_rad": 0.089992,
"max_rad": 0.394489
},
"j5": {
"median_of_take_medians_rad": 0.0293,
"mean_rad": 0.043966,
"max_rad": 0.317013
},
"j6": {
"median_of_take_medians_rad": 0.031,
"mean_rad": 0.054972,
"max_rad": 0.510357
}
}
},
"timestamp_lag": {
"note": "RECORDER ARTEFACT, not robot behaviour: `ur_joint_states`, `tcp_pose` and `wrench` rows are stamped LATE in this release. The GUI recorder writes every row inside its subscription callback and sets t_rel_s to the time that callback RAN; ROS header stamps are not stored. All callbacks share one rclpy spin thread, which a SingleThreadedExecutor services one message per subscription per round. Adding depth recording to this session dropped the round rate to ~67 Hz (41-70 Hz across takes; see `rates`), so every publisher faster than that keeps its KEEP_LAST queue permanently full and each row leaves the queue already `queue_depth / publish_rate` seconds old: /joint_states depth 100 at ~100 Hz -> ~1.0 s, tcp_pose and wrench depth 50 at ~100 Hz -> ~0.5 s. The starvation signature is visible in `rates`: command / ur_joint_states / tcp_pose / wrench, four topics with two different publish rates (250 Hz for command, ~100 Hz for the three controller_manager broadcasters), all converge on the same recorded rate. Streams at or below the round rate never queue and are fresh: cameras (depth header-stamp age measured at 0.022 s), depth, gello_joint_states and gripper. `command` is the one exception to the model rather than an example of it: it publishes at 250 Hz behind a depth-50 queue, so the model predicts ~0.20 s, while the scene-camera cross-correlation puts it at tau ~ 0 and the queue algebra at ~0.08 s. It is treated as fresh on that measurement, to within ~0.08 s. The lag is a PURE DELAY - the waveform is intact - so subtracting the constant below from t_rel_s restores the timebase. NOTHING IS REWRITTEN HERE: the raw files are as recorded. FIX RULE for consumers: subtract ur_joint_states_lag_s from ur_joint_states/t_rel_s and ~0.41 s from tcp_pose/t_rel_s and wrench/t_rel_s (0.900 - 0.495 = 0.405, and speed cross-correlation against command independently gives 0.41); do NOT shift anything else - the gripper and camera tables are already on the camera timebase and shifting them would create a misalignment that is not there. The recorder has since been fixed (header stamps stored, queue depths shrunk, camera/depth writes moved off the spin thread, starvation warning) and depth recording is now opt-in (ENABLE_DEPTH=1, default RGB-only) - this release predates that fix.",
"method": "ur_joint_states_lag_s: joint-space residual. For each candidate lag on a 5 ms grid over 0..1.5 s the ur_joint_states clock is shifted EARLIER by that lag, the six joints are linearly interpolated onto the `command` timestamps, and the lag minimising the median over time of the 6-joint mean |ur_q - cmd| is reported. residual_rad_at_lag / residual_rad_at_zero give that residual with and without the correction; the *_mean variants are the plain mean over all samples and joints, which is larger because it is dominated by the high-velocity transients where the genuine servo following error lives. ur_joint_states_lag_s_mean_objective is the argmin of that plain mean instead of the median: it is the SAME measurement and agrees to within one 5 ms grid step, and it is reported because independent re-measurements of this dataset use it and land on 0.895 s for a minority of takes. Quote the lag as 0.89-0.91 s depending on method and grid. The derived LeRobot release applies the PER-TAKE optimum from the independent re-measurement (carrot_in_pot_lag.json): 0.900 s for 41 takes and 0.895 s for 13, recorded episode by episode in its meta/source_takes.json. That release also DROPS the master frames the shift leaves with no joint sample at or after their corrected lookup time -- the last 26-28 frames (~0.9 s, the post-placement return motion) of every take, 1469 of 18557 -- so it holds 54 episodes / 17088 frames against the 18557 colour frames here. Note the two runs of the mean objective split the ties differently (38 of 54 at 0.900 here, 41 of 54 there); they never differ by more than one 5 ms grid step. xcorr_*: independent check by speed-profile cross-correlation on the same grid (speed, not position, so a constant spatial offset such as the 0.174 m tool offset cannot bias it); a positive value means the second stream is stamped that many seconds later than the first.",
"ur_joint_states_lag_s": {
"min": 0.9,
"median": 0.9,
"max": 0.9,
"n_takes": 54
},
"ur_joint_states_lag_s_mean_objective": {
"min": 0.895,
"median": 0.9,
"max": 0.9,
"n_takes": 54
},
"takes_at_modal_lag": 54,
"modal_lag_s": 0.9,
"residual_rad_at_lag": {
"min": 0.0004,
"median": 0.0007,
"max": 0.0009,
"n_takes": 54
},
"residual_rad_at_lag_mean": {
"min": 0.0016,
"median": 0.0103,
"max": 0.0167,
"n_takes": 54
},
"residual_rad_at_zero": {
"min": 0.034,
"median": 0.0515,
"max": 0.0814,
"n_takes": 54
},
"residual_rad_at_zero_mean": {
"min": 0.0447,
"median": 0.0581,
"max": 0.0782,
"n_takes": 54
},
"xcorr_command_to_ur_joint_states_s": {
"min": 0.58,
"median": 0.905,
"max": 0.94,
"n_takes": 54
},
"xcorr_command_to_tcp_pose_s": {
"min": 0.375,
"median": 0.41,
"max": 1.395,
"n_takes": 54
},
"xcorr_tcp_pose_to_ur_joint_states_s": {
"min": 0.0,
"median": 0.465,
"max": 0.8,
"n_takes": 54
},
"fresh_streams": [
"cam1_frames",
"cam2_frames",
"depth.h5 cam1",
"depth.h5 cam2",
"gello_joint_states",
"gripper",
"command"
],
"late_streams": [
"ur_joint_states",
"tcp_pose",
"wrench"
],
"wrench_note": "`wrench` shares tcp_pose's QoS depth (50) and publisher, so it carries the same ~0.5 s lag. It is not cross-correlated here because the force signal's derivative is too noisy to time reliably; the sibling velocity-correlation analysis measured tcp_pose -> wrench at 0.000 s over 7 of 8 takes, which is the sharpest confirmation of the queue-depth model (equal depth -> equal lag), and tcp_pose -> ur_joint_states at +0.495 s (unequal depth).",
"per_take": [
{
"take": "take_01_20260914_164811",
"ur_joint_states_lag_s": 0.9,
"ur_joint_states_lag_s_mean_objective": 0.9,
"residual_rad_at_lag": 0.0006,
"residual_rad_at_lag_mean": 0.0016,
"n_compared": 503,
"residual_rad_at_zero": 0.0581,
"residual_rad_at_zero_mean": 0.0527,
"xcorr_command_to_ur_joint_states_s": 0.915,
"xcorr_command_to_ur_joint_states_r": 0.824,
"xcorr_command_to_tcp_pose_s": 0.42,
"xcorr_command_to_tcp_pose_r": 0.746,
"xcorr_tcp_pose_to_ur_joint_states_s": 0.46,
"xcorr_tcp_pose_to_ur_joint_states_r": 0.75
},
{
"take": "take_02_20260914_164845",
"ur_joint_states_lag_s": 0.9,
"ur_joint_states_lag_s_mean_objective": 0.9,
"residual_rad_at_lag": 0.0007,
"residual_rad_at_lag_mean": 0.0086,
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