{ "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": { "take_01_20260914_164811": -1, "take_02_20260914_164845": -1, "take_03_20260914_164930": 1, "take_04_20260914_165011": 1, "take_05_20260914_165156": -1, "take_08_20260914_165353": 1, "take_09_20260914_165429": 1, "take_11_20260914_165557": 1, "take_13_20260914_165647": -1, "take_16_20260914_165825": -1, "take_17_20260914_165859": -1, "take_20_20260914_170107": -1, "take_21_20260914_170135": 1, "take_23_20260914_170302": 1, "take_25_20260914_170407": -1, "take_26_20260914_170447": -1, "take_27_20260914_170519": 1, "take_30_20260914_170700": -1, "take_32_20260914_170803": 1, "take_36_20260914_171037": 1, "take_40_20260914_171256": 1, "take_44_20260914_171505": -1, "take_54_20260914_172116": 1, "take_55_20260914_172155": -1 }, "takes_with_depth_cam1_colour_diff": { "take_04_20260914_165011": -1, "take_05_20260914_165156": 1, "take_08_20260914_165353": -1, "take_09_20260914_165429": -1, "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:= 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": 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