DATA_DICTIONARY.md

Data Dictionary — Bigenlight/carrot_in_pot_raw

Raw teleoperation recordings for the task "Put carrot in pot", captured via a GELLO leader → UR7e follower setup with two Intel RealSense D435 cameras — colour and depth — in a single session on 2026-09-14.

This document describes the RAW release: the original per-take HDF5 signal logs, the original camera MP4s, and the original compressed-depth HDF5s, exactly as recorded. For a ready-to-train version see the LeRobot dataset carrot_in_pot_lerobot_v3.

• Scale: 54 takes (take_*) · 18,557 cam1 colour frames (18,557 cam2) · 18,541 cam1 depth frames (18,538 cam2) · 619.33 s (10.3 min) · 3.73 GB
• Every take folder take_NN_20260914_HHMMSS/ contains exactly four files: vectors.h5, depth.h5, cam1.mp4, cam2.mp4. Measured: no hidden or stray files, no sub-directories, in any of the 54 folders.
• Verified read-only against all 54 vectors.h5, all 54 depth.h5 and all 108 MP4s; no absolute-path leakage and no PII inside the HDF5 files.
• Every figure in this document was measured from the files themselves by make_carrot_raw_stats.py. The machine-readable version is dataset_stats.json.
• ⚠️ Read §3.6 before you align streams. Three of the nine groups — ur_joint_states, tcp_pose and wrench — carry timestamps that are late by a constant (0.900 s and ≈0.41 s). It is a recorder artefact, it is a pure delay, and it is not corrected in these files.

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1. Hardware & recording setup

Component	Spec
Robot (follower)	Universal Robots UR7e — 6-DOF collaborative arm. Joint angles/velocities in radians / rad·s⁻¹. This is the arm that executes and the only arm used as policy input at inference.
Teleoperation (leader)	GELLO — low-cost 3D-printed 6-DOF leader arm, moved by hand. The recorded gello_* streams are the leader signal. Because this session ran in EEF mode (next row) they are not a joint-space mirror of the follower — see §3.4 and §9. Do not use gello_* as an input feature.
Control mode	End-effector (EEF) delta teleop — ur7e_gello_real.launch.py robot_ip:=<ROBOT_IP> headless_mode:=true control_mode:=eef. The bridge computes the leader's end-effector pose by forward kinematics of a virtual leader chain, takes the pose delta, and solves IK on the UR7e; the resulting absolute joint targets are what command contains. This is the first EEF-mode release in the family — cube_in_cup_raw and banana_in_pot_raw are joint-mode recordings.
Gripper	Robotiq 2F-85 two-finger parallel gripper. Continuous normalized command (grip_cmd), continuous measured position (grip_pos), and the leader-side trigger (gello_grip). 0.0 = open, 1.0 = closed in all three.
Camera 1 — scene	Intel RealSense D435, serial 143322071682, on a black desk-clamp camera stand to the left of the robot, looking down at the work surface (third-person view). Colour 1280×720 @ 30 fps + depth 848×480 @ 30 fps.
Camera 2 — wrist	Intel RealSense D435, serial 143322072540, strapped to the robot wrist just above the gripper (eye-in-hand). Colour 1280×720 @ 30 fps + depth 848×480 @ 30 fps.
Camera model	Both are plain D435 (no D435if in this session). Both on a powered USB-3 hub.
Video format	1280×720, 30 fps, MPEG-4 (mpeg4), yuv420p. Measured identical across all 108 videos (ffprobe: mpeg4 1280 720 yuv420p 30/1). The LeRobot copy re-encodes; RAW keeps MPEG-4.
Depth format	848×480 uint16 millimetres, one 16-bit PNG per frame stored verbatim in depth.h5 (§5). aligned_to_color = False.
Scene	A white desk holding one plastic carrot (orange-red body, green leaves) and one silver steel saucepan with a black handle. Small yellow sticky notes on the table are placement markers for the operator. Grey felt partition and white shelving behind.

The cam1 ↔ cam2 mapping above is asserted and verified from the imagery: cam1 shows the table from outside (pot + carrot, robot entering from the right), cam2 shows the gripper fingers at the bottom of every frame. See assets/take01_cam1_first_frame.jpg and assets/take01_cam2_first_frame.jpg.

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2. Files in a take folder

File	What it is	Total bytes (54 takes)
vectors.h5	all robot/teleop/gripper/camera-timeline signals, multi-rate (§3)	38.7 MB
depth.h5	per-camera compressed depth frames + intrinsics/extrinsics (§5)	3.29 GB
cam1.mp4	scene camera colour video, 1280×720 @ 30 fps	185.4 MB
cam2.mp4	wrist camera colour video, 1280×720 @ 30 fps	218.8 MB

Total 3,729,216,578 bytes (3.73 GB). Depth dominates: measured 88.6 kB per depth frame averaged over all 37,079 stored depth frames.

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3. vectors.h5 — top-level layout

One file per take. Root has 9 groups (schema measured identical in all 54 files), each a time-series recorded at its own native rate on its own clock. Each group has a t_rel_s dataset = seconds since take start. All datasets are 1-D float64, one array per channel (columnar layout — a channel foo is stored as dataset group/foo, NOT as a 2-D table).

Group	Rows (all 54 takes)	Native rate	What it is
cam1_frames	18,557	30.0 Hz	Timestamp + frame index for each cam1.mp4 frame
cam2_frames	18,557	30.0 Hz	Timestamp + frame index for each cam2.mp4 frame
command	40,640	~67 Hz	Commanded UR7e joint targets (the action), radians. t_rel_s fresh
ur_joint_states	40,629	~67 Hz	Measured UR7e joint state: position, velocity, effort. ⚠️ t_rel_s 0.900 s late (§3.6)
tcp_pose	40,619	~67 Hz	Measured tool-center-point pose (position + quaternion). ⚠️ t_rel_s ≈0.41 s late (§3.6)
wrench	40,621	~67 Hz	6-axis force/torque at the TCP. ⚠️ t_rel_s ≈0.41 s late (§3.6)
gripper	24,015	~38.8 Hz	Gripper command, measured position, leader trigger
gello_joint_states	18,573	30.0 Hz	GELLO leader joint pos + vel (teleop only — see §3.4)
synchronized	0 (EMPTY)	—	Intended fused/aligned table; empty in all 54 takes — ignore

3.1 Measured rates (all 54 takes, plus the two depth.h5 streams)

Two different rate statistics are reported because they disagree, and both are true.

Group	mean rate (Hz)	min–max across takes	median Δt (ms)	5th pct Δt (ms)	max gap (ms)	1 / median Δt (Hz)
cam1_frames	30.00	29.94 – 30.03	33.0	30.0	55.1	30.3
cam2_frames	30.00	29.96 – 30.09	33.1	30.0	61.0	30.2
command	67.33	41.22 – 70.37	15.9	4.3	44.1	62.9
ur_joint_states	67.34	41.15 – 70.38	15.9	4.3	43.3	62.9
tcp_pose	67.34	41.16 – 70.34	15.9	4.2	40.2	62.9
wrench	67.34	41.16 – 70.34	15.9	4.2	41.0	62.9
gripper	38.83	37.58 – 41.14	31.4	2.2	69.4	31.9
gello_joint_states	30.01	29.96 – 30.06	33.1	20.7	69.6	30.2
depth.h5 cam1	30.00	29.92 – 30.07	33.0	20.2	68.8	30.3
depth.h5 cam2	30.00	29.92 – 30.10	32.9	19.9	69.2	30.4

Mean rate = (N−1)/(t_last − t_first) per take, then the median across takes. Δt percentiles are pooled over all takes.

Why the two rate columns differ: the robot streams are bursty. The inter-sample interval for command / ur_joint_states / tcp_pose / wrench is bimodal — 5 % of intervals are ≤ 4.3 ms (samples arriving back-to-back) while the median is 15.9 ms. Averaging over the whole take gives ~67 Hz, while the typical spacing corresponds to ~63 Hz. Logging is timestamp-driven, not fixed-period. Always resample using t_rel_s; never assume a fixed dt and never index-align across streams.

Four slow takes. take_24, take_25, take_26 and take_28 log their robot streams at 41.2 – 49.2 Hz against a 67.3 Hz median for the other 50 takes. Nothing else about them is unusual (durations 8.9–10.1 s, no NaN, frame counts match). They simply have fewer robot samples per camera frame — ~1.4 instead of ~2.2. Per-take rates are in §7 and dataset_stats.json.

Comparison with the sibling dataset. These robot streams run at ~67 Hz, versus ~97–98 Hz in cube_in_cup_raw — about 2.2 robot samples per camera frame here against ~3.2 there. Same rig family, different session.

3.2 ⚠️ The columns attribute quirk (still present — read this)

Every group carries an HDF5 attribute named columns. It is a single scalar JSON string, e.g. '["t_rel_s", "q1", ...]' — NOT a native list/array. Measured: h5py returns Python str in all 54 files (dataset_stats.json → aggregate.columns_attr_python_types == ["str"]). If a naive consumer does list(grp.attrs["columns"]) expecting a list, it iterates the string character-by-character and you get ['[', '"', 't', '_', 'r', ...]. Always json.loads(grp.attrs["columns"]). The correct per-group column lists are given verbatim below and do not depend on the attribute.

3.3 Per-group / per-channel schema

All datasets float64, shape (N,) where N = that group's row count for the take. Ranges are measured pooled across all 54 takes.

cam1_frames — scene-camera frame timeline

Channel	dtype	Unit	Meaning	Range
t_rel_s	float64	s	Time of this frame, since take start	0 → 17.76
frame_idx	float64	index	0-based frame number in cam1.mp4 (float-typed)	0 → 532

cam2_frames — wrist-camera frame timeline

Channel	dtype	Unit	Meaning	Range
t_rel_s	float64	s	Time of this frame, since take start	0 → 17.78
frame_idx	float64	index	0-based frame number in cam2.mp4 (float-typed)	0 → 532

command — commanded UR7e joint targets ➜ the ACTION

Channel	dtype	Unit	Meaning	min	max	mean
t_rel_s	float64	s	Timestamp — fresh (this table did not queue; §3.6)	—	—	—
cmd1	float64	rad	Target angle, joint 1 (base)	−3.7024	−2.6804	−3.1542
cmd2	float64	rad	Target angle, joint 2 (shoulder)	−1.7302	−0.9591	−1.2976
cmd3	float64	rad	Target angle, joint 3 (elbow)	1.2936	2.0710	1.7272
cmd4	float64	rad	Target angle, joint 4 (wrist 1)	−2.5877	−1.5437	−2.0579
cmd5	float64	rad	Target angle, joint 5 (wrist 2)	−1.8701	−1.2600	−1.5747
cmd6	float64	rad	Target angle, joint 6 (wrist 3)	−4.4081	−2.0573	−3.3696

ur_joint_states — measured UR7e joint state ➜ core of observation.state

Channel	dtype	Unit	Meaning	min	max	mean
t_rel_s	float64	s	Timestamp — ⚠️ stamped 0.900 s late; subtract it (§3.6)	—	—	—
q1	float64	rad	Measured angle, joint 1	−3.7028	−2.6809	−3.1325
q2	float64	rad	Measured angle, joint 2	−1.7284	−0.9593	−1.3023
q3	float64	rad	Measured angle, joint 3	1.3382	2.0708	1.7311
q4	float64	rad	Measured angle, joint 4	−2.5867	−1.5425	−2.0613
q5	float64	rad	Measured angle, joint 5	−1.8701	−1.2621	−1.5680
q6	float64	rad	Measured angle, joint 6	−4.4087	−2.0574	−3.3592
qd1	float64	rad·s⁻¹	Measured velocity, joint 1	−0.3854	0.4454	−0.0110
qd2	float64	rad·s⁻¹	Measured velocity, joint 2	−0.4081	0.4219	0.0198
qd3	float64	rad·s⁻¹	Measured velocity, joint 3	−0.5409	0.6098	0.0042
qd4	float64	rad·s⁻¹	Measured velocity, joint 4	−0.6037	0.6094	−0.0284
qd5	float64	rad·s⁻¹	Measured velocity, joint 5	−0.5854	0.5897	−0.0031
qd6	float64	rad·s⁻¹	Measured velocity, joint 6	−0.5925	0.5966	−0.0159
eff1	float64	N·m (motor effort / current proxy)	Effort, joint 1	−2.1749	2.3716	−0.0579
eff2	float64	N·m	Effort, joint 2	−6.2647	2.9379	−2.8383
eff3	float64	N·m	Effort, joint 3	−4.2612	3.6107	−1.9856
eff4	float64	N·m	Effort, joint 4	−0.8316	2.0361	−0.3670
eff5	float64	N·m	Effort, joint 5	−0.6181	1.0086	0.0195
eff6	float64	N·m	Effort, joint 6	−0.4995	0.4773	−0.0333

Follower tracking. The median per-take |cmd_i − q_i| (nearest-timestamp aligned onto the command clock) is 0.023 / 0.061 / 0.033 / 0.080 / 0.029 / 0.031 rad for joints 1–6, with a worst single sample of 0.510 rad (joint 6).

⚠️ Those figures are computed on the raw timestamps, and most of that gap is the §3.6 timestamp artefact, not tracking error. Aligned on the corrected clock (ur_joint_states/t_rel_s − 0.900 s) the same residual collapses to 0.0007–0.0103 rad (median 0.0062) — and to 0.0004–0.0009 rad on the robust objective of §3.6. What is left there is the genuine servo following error, and it lives in the high-velocity transients. The uncorrected numbers are kept above because they describe the files as they are shipped, which is what a consumer who skips §3.6 will see.

command is a target, not a measurement — if you need where the arm actually was, use ur_joint_states (on its corrected clock).

tcp_pose — measured tool-center-point pose (robot base frame)

Channel	dtype	Unit	Meaning	min	max	mean
t_rel_s	float64	s	Timestamp — ⚠️ stamped ≈0.41 s late; subtract it (§3.6)	—	—	—
x	float64	m	TCP position x in robot base frame	0.4148	0.7095	0.5674
y	float64	m	TCP position y	−0.2141	0.3900	0.1307
z	float64	m	TCP position z	0.0175	0.3066	0.1424
qx	float64	unit quaternion	Orientation x	−0.9577	0.8983	0.2690
qy	float64	unit quaternion	Orientation y	−0.9941	0.9943	0.3223
qz	float64	unit quaternion	Orientation z	−0.1567	0.1575	−0.0030
qw	float64	unit quaternion	Orientation w	0.000001	0.1547	0.0365

Workspace extent: 29 cm in x, 60 cm in y, 29 cm in z. qw stays near 0 while qx/qy dominate: the tool points essentially straight down throughout, as expected for a top-down pick-and-place. The z floor of 0.0175 m is the fingers reaching the table to pick the carrot.

wrench — 6-axis force/torque at TCP

Channel	dtype	Unit	Meaning	min	max	mean
t_rel_s	float64	s	Timestamp — ⚠️ stamped ≈0.41 s late; subtract it (§3.6)	—	—	—
fx	float64	N	Force along x	−18.711	36.708	−1.179
fy	float64	N	Force along y	−20.573	35.621	−0.536
fz	float64	N	Force along z	−114.883	7.142	0.987
tx	float64	N·m	Torque about x	−10.870	6.667	0.106
ty	float64	N·m	Torque about y	−7.891	9.525	−0.152
tz	float64	N·m	Torque about z	−0.722	0.817	−0.065

The wide fz excursion (down to −114.9 N) is a transient contact/acceleration spike; the mean is +0.99 N with a standard deviation of 3.76 N, so the stream sits near zero the overwhelming majority of the time.

gripper — gripper signals

Channel	dtype	Unit	Meaning	min	max	mean
t_rel_s	float64	s	Timestamp	—	—	—
gello_grip	float64	normalized 0–1	Leader (GELLO) grip trigger — teleop only	0.0000	1.0000	0.3883
grip_cmd	float64	normalized 0–1 (0 = open → 1 = closed)	Commanded gripper (part of the action)	0.0001	0.9998	0.3904
grip_pos	float64	normalized (0 = open → 1 = closed)	Measured gripper opening (part of observation.state)	0.0118	0.8980	0.2385

grip_pos 0.0118 is fully open and larger is more closed. Two plateaus matter:

• holding the carrot: measured 0.4745 – 0.6627 across the 54 final grasps (median 0.5569). The fingers stop on the carrot, so this value is the carrot's width.
• closed on nothing: 0.8980. This appears in exactly two takes (take_39, take_40) and is the signature of the missed first grasp described in §9.

gello_joint_states — GELLO leader joints (teleop only, NOT for inference)

Channel	dtype	Unit	Meaning	min	max
t_rel_s	float64	s	Timestamp	—	—
q1	float64	rad	Leader joint 1 angle	−1.0048	0.5752
q2	float64	rad	Leader joint 2 angle	−1.9545	−0.5724
q3	float64	rad	Leader joint 3 angle	0.6025	2.4248
q4	float64	rad	Leader joint 4 angle	−3.2409	−1.5090
q5	float64	rad	Leader joint 5 angle	−2.0874	−1.0504
q6	float64	rad	Leader joint 6 angle	−2.1833	0.8279
qd1…qd6	float64	rad·s⁻¹	Leader joint velocities 1–6	−2.703	2.203

3.4 ⚠️ The leader stream does not mirror the follower in this session

This is the single largest difference from cube_in_cup_raw, where leader joints 1–5 tracked the follower to within ±0.011 rad and joint 6 was a clean +2π wrap. Measured here (gello_qi − ur_qi, nearest-timestamp aligned, pooled over all 54 takes, n = 18,573 per joint):

joint	median diff (rad)	p05 → p95 (rad)	per-take median: min → max	per-take median std	r(gello_qi, ur_qi)	r(gello_qi, cmd_i)
1	+2.7019	+2.149 → +3.563	+2.367 → +3.014	0.160	−0.785	−0.956
2	−0.0147	−0.508 → +0.399	−0.516 → +0.401	0.203	+0.078	+0.261
3	−0.0242	−0.531 → +0.500	−0.471 → +0.531	0.225	−0.011	−0.059
4	−0.3328	−0.695 → +0.119	−0.604 → +0.108	0.167	+0.537	+0.603
5	−0.0247	−0.303 → +0.329	−0.354 → +0.380	0.132	+0.583	+0.435
6	+2.8886	+2.248 → +3.725	+2.414 → +3.303	0.229	+0.025	−0.031

(r = Pearson correlation computed per take, then averaged over the 54 takes.)

Three things follow, and all three are measurements, not opinions:

1. There is no single offset to subtract. The per-take median of the difference itself moves by 0.13–0.23 rad (std column), and within a take the difference has a standard deviation of 0.03–0.84 rad. Joint 6 is not a 2π wrap here (+2.889 rad ≠ 6.283 rad).
2. Joint 1 is anti-correlated with the follower (r = −0.785 against ur_q1, −0.956 against cmd1, and −0.599 on first differences). The leader's joint-1 signal moves the opposite way to the arm it is driving.
3. Therefore gello_* cannot be used to reconstruct the action, the follower state, or a leader/follower residual in this release. It is retained because this is the raw release and because it is the operator's own hand signal at 30 Hz, but it is leader-frame telemetry with its own zeros and its own sign conventions. The LeRobot conversion drops it.

Why: this session was recorded in end-effector (EEF) delta teleop mode, not joint mode (§1). The bridge takes the leader's end-effector pose delta — obtained by forward kinematics of a virtual leader chain — and solves IK on the UR7e to produce command. The leader's own joint angles are therefore a different kinematic solution of a different chain: they are related to the follower only through the end-effector pose, never joint-by-joint. Under that mapping the joint-1 anti-correlation is expected and joint 6 has no reason to be a 2π wrap. The joint-mode siblings cube_in_cup_raw and banana_in_pot_raw show ≈0 offsets for exactly the same reason, in reverse. command remains the true absolute joint target in both modes, so nothing downstream changes: use command for the action and ur_joint_states for the state.

3.5 synchronized — EMPTY in all 54 takes (do not use)

The group exists and declares 56 channels, but every dataset has shape (0,) in every take. Fusion is done downstream at conversion time, not stored here. Ignore it. The declared header, in full:

t_rel_s, t_wall,
gello_q1, gello_q2, gello_q3, gello_q4, gello_q5, gello_q6,
gello_qd1, gello_qd2, gello_qd3, gello_qd4, gello_qd5, gello_qd6,
gello_grip,
cmd1, cmd2, cmd3, cmd4, cmd5, cmd6,
ur_q1, ur_q2, ur_q3, ur_q4, ur_q5, ur_q6,
ur_qd1, ur_qd2, ur_qd3, ur_qd4, ur_qd5, ur_qd6,
ur_eff1, ur_eff2, ur_eff3, ur_eff4, ur_eff5, ur_eff6,
grip_cmd, grip_pos,
fx, fy, fz, tx, ty, tz,
tcp_x, tcp_y, tcp_z, tcp_qx, tcp_qy, tcp_qz, tcp_qw,
cam1_frame_idx, cam2_frame_idx

3.6 ⚠️ Recorder timestamp artefact — three groups are stamped late

The arm was not late; the recording is. In this release the t_rel_s of three groups is behind the rest of the take by a constant: their rows describe the robot as it was a fraction of a second earlier than they claim. Nothing else in the file is affected, and nothing here has been rewritten.

group	stamped late by	per-take spread	how it was measured
ur_joint_states	0.900 s	0.895 – 0.900 s, median 0.900 s; most takes land exactly on 0.900 and the rest one 5 ms grid step below (38 of 54 at 0.900 in this release's own measurement, 41 of 54 in an independent re-measurement — same lag, different tie-break)	joint-space residual: shift the ur_joint_states clock by −τ, linearly interpolate q1..q6 onto the command timestamps, take the τ on a 5 ms grid over 0–1.5 s that minimises mean |ur_q − cmd|
tcp_pose	≈ 0.41 s (= 0.900 − 0.495; speed cross-correlation against command independently gives 0.41. Its absolute queue age is ≈0.50 s — the difference is command's own residual ≈0.08 s)	0.495 – 0.500 s ahead of ur_joint_states (median 0.495) — exactly the half-queue prediction	forward-kinematics residual: fk(q) ⊕ 0.174 m against tcp_pose, 11.2 – 23.3 mm at zero shift and 0.52 – 2.89 mm at the optimum
wrench	≈ 0.41 s	measured 0.000 s from tcp_pose in 7 of 8 takes	velocity cross-correlation; wrench shares tcp_pose's QoS depth (50) and publisher, so it carries the same age

Fresh — do not shift these: cam1_frames, cam2_frames, both depth.h5 streams, gello_joint_states, gripper, and command. ⚠️ For all of them but command that is the queue model and the measurement; for command it is the measurement alone — it is published at 250 Hz behind a depth-50 queue, so the model predicts ~0.20 s, while the scene-camera cross-correlation (evidence 2 below) puts it at τ ≈ 0 and the queue algebra at ≈0.08 s. Treat command as fresh to within ≈0.08 s — under three camera frames — not to zero.

Three independent observations make "the robot was slow" impossible and pin the blame on the recorder:

1. Depth frames carry the driver's own header.stamp (depth.h5/<cam>/stamp_s, §5), and their measured age at the moment they were written is 0.022 s. The images are fresh; that is the absolute reference point the other streams are compared against.
2. The scene camera sees the arm move when command says it moved. Frame-to-frame motion energy in cam1.mp4 correlates with the command TCP speed at τ ≈ 0 (−0.055 s and +0.010 s on the two takes checked) and with ur_joint_states at +0.865 / +0.935 s. If the arm had lagged, the video would have lagged with it.
3. tcp_pose and wrench are 0.000 s apart. Both are measurements published in the same controller_manager cycle from the same RTDE packet, so their physical time difference is zero — and that is exactly what is measured. Meanwhile tcp_pose → ur_joint_states, also two measurements from that same packet, sit 0.495–0.500 s apart (median 0.495, measured on all 54 takes by forward-kinematics residual; the sibling velocity-correlation analysis independently reports 0.495 s, and the release script's speed cross-correlation 0.465 s). Two streams out of one packet cannot physically disagree; only their stamps can.

Mechanism. The recorder writes each row inside its ROS subscription callback and stamps it with the time that callback ran — message header stamps were not stored — and every callback shares one rclpy spin thread, which a SingleThreadedExecutor services one message per subscription per round. Adding depth recording to this session (≈5.3 MB/s of HDF5 writes plus four extra 30 Hz callbacks on that same thread) dropped the round rate from ~98–100 Hz to ≈67 Hz — 41–70 Hz across takes (§3.1), so every publisher faster than that kept its KEEP_LAST queue permanently full and each row emerged already queue_depth ÷ publish_rate old:

topic	QoS depth	publish rate	predicted age	measured
/joint_states → ur_joint_states	100	~100 Hz	1.00 s	0.900 s vs command
/tcp_pose_broadcaster/pose → tcp_pose	50	~100 Hz	0.50 s	0.495 s ahead of ur_joint_states — half the queue, half the age
/force_torque_sensor_broadcaster/wrench → wrench	50	~100 Hz	0.50 s	0.000 s from tcp_pose
/forward_position_controller/commands → command	50	250 Hz (publish_rate_hz)	0.20 s	≈0.08 s — the one row where the model over-predicts (see the Fresh note above)
/gello/joint_states, gripper, colour, depth	10–50	30–40 Hz	~0	0.022 s (depth, header-stamped)

The starvation signature is visible in §3.1: command, ur_joint_states, tcp_pose and wrench have two different publish rates — 250 Hz for command, ~100 Hz for the three controller_manager broadcasters — yet converge on the same recorded rate to five decimals (take_01: 59.79218 / 59.79089 / 59.79282 / 59.79153 Hz). Streams that were not starved keep their own native rate (gello_joint_states 29.99–30.06 Hz, cam1_frames 29.96–30.01 Hz).

It is a pure delay, not a distortion. The waveform survives the shift intact, which is both why one constant recovers it and how we know it is not servo lag — a real tracking lag is a low-pass and would smear the shape, so no shift could collapse the residual like this:

residual	uncorrected (τ = 0)	at the per-take optimum
ur_joint_states vs command, mean |Δq| over 6 joints	0.049 – 0.090 rad (median 0.063)	0.0007 – 0.0103 rad (median 0.0062)
same, on the release script's median-over-time objective	0.034 – 0.081 rad	0.0004 – 0.0009 rad
tcp_pose vs fk(q) ⊕ 0.174 m	11.2 – 23.3 mm (median 17.6)	0.52 – 2.89 mm (median 1.61)

⚠️ Quote the lag with its method attached. Four estimators land within ±0.02 s of one another and none of them is wrong; published numbers that look contradictory are the same measurement:

estimator	grid	result
joint-space residual, mean |ur_q − cmd| over 6 joints	5 ms	0.895 – 0.900 s (median 0.900) — dataset_stats.json → timestamp_lag.ur_joint_states_lag_s_mean_objective
same, median-over-time objective	5 ms	0.900 s in all 54 takes — dataset_stats.json → timestamp_lag.ur_joint_states_lag_s
velocity cross-correlation, command → ur_joint_states	5 ms	0.875 – 0.915 s (median 0.900; the sibling analysis reports 0.910 s)
TCP-position residual	10 ms	0.90 s in 11 of 11 takes checked (residual median 0.42 – 0.64 mm, against 44 – 67 mm at τ = 0)

So: 0.89 – 0.91 s depending on method and grid. The derived LeRobot release applies the per-take optimum from the first row — 0.900 s for 41 takes, 0.895 s for 13 — and records it episode by episode. It also drops the frames that shift leaves without a joint sample — the last 26–28 master frames (~0.9 s) of every take, 1,469 of 18,557 — so that release holds 54 episodes / 17,088 frames. A single 0.900 s for everything is within one 5 ms grid step of that, i.e. a seventh of a camera frame, and is perfectly usable.

The second-order lags in the session are worth knowing so you do not mistake one for the other: gello_joint_states → command is +0.25 s and is real — the leader→command latency of the One-Euro filter and slew limiter, measured at +0.253 s in the joint-mode sibling session where nothing was starved and +0.260 s here. grip_cmd → grip_pos is +0.37 s and is also real: the Robotiq's open/close time (+0.34 s in the sibling). Neither is a stamping artefact and neither should be corrected.

Fix rule for consumers

import h5py, numpy as np

UR_LAG_S, TCP_LAG_S = 0.900, 0.405         # 0.405 = 0.900 - 0.495; wrench uses TCP_LAG_S too
with h5py.File("take_01_20260914_164811/vectors.h5", "r") as f:
    cam_t = f["cam1_frames"]["t_rel_s"][:]                      # master clock, already correct
    ur_t  = f["ur_joint_states"]["t_rel_s"][:] - UR_LAG_S       # <- the whole fix
    ur_q  = np.stack([f["ur_joint_states"][f"q{i}"][:] for i in range(1, 7)], 1)
ur_on_cam = np.stack([np.interp(cam_t, ur_t, ur_q[:, k]) for k in range(6)], 1)

Do not shift anything else. command, gripper, gello_joint_states, the colour frames and the depth frames already share one correct timebase; applying the same offset to them would create a 0.9 s misalignment that is not in the data — including the two real lags listed above, which are physics, not stamping.

What this means for the derived release. The LeRobot conversion builds observation.state[0:6] from ur_joint_states, so it shifts that table by that take's measured τ (−0.900 s for 41 takes, −0.895 s for 13) before resampling onto the 30 fps camera grid; grip_pos, action (command + grip_cmd), the RGB videos and both depth streams are resampled unchanged. The applied τ is recorded per episode there in meta/source_takes.json (ur_joint_states_lag_s). After the shift the last ~0.9 s of every take has no ur_joint_states sample at or after its corrected lookup time; rather than repeat the final joint row under a still-moving arm, that release drops those frames whole — the last 26–28 master frames per take, 1,469 of 18,557, leaving 54 episodes / 17,088 frames. The dropped window is the arm's post-placement return motion, and it exists only here. tcp_pose and wrench are not part of that release — take them from here and subtract ≈0.41 s yourself.

Status. The raw files are published exactly as recorded: a documented constant is recoverable, a silently re-stamped file is not. Per-take values, both residual definitions and the cross-correlation checks are in dataset_stats.json under timestamp_lag. The recorder itself was fixed after this release — header stamps are now stored alongside t_rel_s, the high-rate queue depths were shrunk, camera/depth writes were moved off the spin thread, and it now warns when the recorded rates of unrelated topics converge (the signature above); depth recording is now opt-in (ENABLE_DEPTH=1), default RGB-only. Later sessions in this family are not affected; the sibling joint-mode releases cube_in_cup_raw / banana_in_pot_raw, recorded before depth existed, show ≈0.05 s.

---

4. Complete pooled value ranges

Every channel that carries a value (the per-group t_rel_s clocks are excluded; their spans are the take durations in §7). Pooled over all 54 takes.

channel	min	max	mean	std	n
cam1_frames/frame_idx	0.0000	532.0000	177.0869	109.6896	18,557
cam2_frames/frame_idx	0.0000	532.0000	177.0590	109.6378	18,557
command/cmd1	−3.7024	−2.6804	−3.1542	0.1980	40,640
command/cmd2	−1.7302	−0.9591	−1.2976	0.1420	40,640
command/cmd3	1.2936	2.0710	1.7272	0.1178	40,640
command/cmd4	−2.5877	−1.5437	−2.0579	0.1856	40,640
command/cmd5	−1.8701	−1.2600	−1.5747	0.1043	40,640
command/cmd6	−4.4081	−2.0573	−3.3696	0.3043	40,640
gello_joint_states/q1	−1.0048	0.5752	−0.3531	0.2950	18,573
gello_joint_states/q2	−1.9545	−0.5724	−1.3268	0.2227	18,573
gello_joint_states/q3	0.6025	2.4248	1.7092	0.2698	18,573
gello_joint_states/q4	−3.2409	−1.5090	−2.3761	0.2560	18,573
gello_joint_states/q5	−2.0874	−1.0504	−1.5830	0.1967	18,573
gello_joint_states/q6	−2.1833	0.8279	−0.3985	0.5574	18,573
gello_joint_states/qd1	−1.1920	1.3504	0.0152	0.1502	18,573
gello_joint_states/qd2	−0.9621	0.8062	−0.0023	0.1170	18,573
gello_joint_states/qd3	−1.0662	1.2119	0.0069	0.1484	18,573
gello_joint_states/qd4	−0.8872	1.4505	−0.0018	0.1864	18,573
gello_joint_states/qd5	−1.3970	1.3019	−0.0049	0.1201	18,573
gello_joint_states/qd6	−2.7027	2.2032	0.0172	0.2816	18,573
gripper/gello_grip	0.0000	1.0000	0.3883	0.4728	24,015
gripper/grip_cmd	0.0001	0.9998	0.3904	0.4697	24,015
gripper/grip_pos	0.0118	0.8980	0.2385	0.2616	24,015
tcp_pose/qw	0.0000	0.1547	0.0365	0.0265	40,619
tcp_pose/qx	−0.9577	0.8983	0.2690	0.5684	40,619
tcp_pose/qy	−0.9941	0.9943	0.3223	0.7042	40,619
tcp_pose/qz	−0.1567	0.1575	−0.0030	0.0533	40,619
tcp_pose/x	0.4148	0.7095	0.5674	0.0417	40,619
tcp_pose/y	−0.2141	0.3900	0.1307	0.1296	40,619
tcp_pose/z	0.0175	0.3066	0.1424	0.0643	40,619
ur_joint_states/eff1	−2.1749	2.3716	−0.0579	0.9634	40,629
ur_joint_states/eff2	−6.2647	2.9379	−2.8383	1.1063	40,629
ur_joint_states/eff3	−4.2612	3.6107	−1.9856	0.9204	40,629
ur_joint_states/eff4	−0.8316	2.0361	−0.3670	0.2433	40,629
ur_joint_states/eff5	−0.6181	1.0086	0.0195	0.2220	40,629
ur_joint_states/eff6	−0.4995	0.4773	−0.0333	0.2094	40,629
ur_joint_states/q1	−3.7028	−2.6809	−3.1325	0.1858	40,629
ur_joint_states/q2	−1.7284	−0.9593	−1.3023	0.1471	40,629
ur_joint_states/q3	1.3382	2.0708	1.7311	0.1170	40,629
ur_joint_states/q4	−2.5867	−1.5425	−2.0613	0.1840	40,629
ur_joint_states/q5	−1.8701	−1.2621	−1.5680	0.1014	40,629
ur_joint_states/q6	−4.4087	−2.0574	−3.3592	0.3053	40,629
ur_joint_states/qd1	−0.3854	0.4454	−0.0110	0.1080	40,629
ur_joint_states/qd2	−0.4081	0.4219	0.0198	0.1153	40,629
ur_joint_states/qd3	−0.5409	0.6098	0.0042	0.1150	40,629
ur_joint_states/qd4	−0.6037	0.6094	−0.0284	0.1933	40,629
ur_joint_states/qd5	−0.5854	0.5897	−0.0031	0.1031	40,629
ur_joint_states/qd6	−0.5925	0.5966	−0.0159	0.1489	40,629
wrench/fx	−18.7112	36.7076	−1.1785	0.8813	40,621
wrench/fy	−20.5731	35.6206	−0.5359	1.1133	40,621
wrench/fz	−114.8831	7.1419	0.9871	3.7562	40,621
wrench/tx	−10.8697	6.6670	0.1063	0.2696	40,621
wrench/ty	−7.8906	9.5245	−0.1523	0.2298	40,621
wrench/tz	−0.7222	0.8171	−0.0647	0.1000	40,621

---

5. depth.h5 — the depth stream

New in this release relative to the cube/banana siblings. One file per take, written by the recorder's depth_writer.py. Layout:

/cam1/                          <- scene camera; attrs below
    png        vlen uint8  (N,)   a COMPLETE 16-bit PNG file per frame, verbatim
    t_rel_s    float64     (N,)   session-relative capture time, same origin as vectors.h5
    frame_idx  int64       (N,)   0-based running index (== row)
    stamp_s    float64     (N,)   ROS header stamp (s), NaN if unknown
    camera_info/                  attrs: width, height, distortion_model, frame_id, D, K, R, P
    extrinsics_depth_to_color/    attrs: rotation (9, COLUMN-major), translation (3, metres),
                                         layout='column_major'
/cam2/                          <- wrist camera; identical structure

5.1 Group attributes (measured identical in all 54 takes, both cameras)

attr	value	meaning
encoding	16UC1	single-channel 16-bit unsigned
unit	mm	millimetres
depth_scale_m	0.001	multiply by this to get metres
container	png	each png[i] cell is a whole PNG file
header_bytes_stripped	12	the compressed_depth_image_transport ConfigHeader was already removed on write — the cell starts at the PNG magic
width / height	848 / 480	depth resolution, not the 1280×720 colour resolution
aligned_to_color	False	frames are in the depth optical frame; see §5.4
source_topic	/cam1/cam1/depth/image_rect_raw/compressedDepth (and cam2)	the ROS topic that fed it

0 means no return / invalid — not "zero distance". Always mask with d > 0.

5.2 camera_info (the depth imager, measured identical across all 54 takes per camera)

	cam1 (scene)	cam2 (wrist)
width × height	848 × 480	848 × 480
fx, fy	426.74179077, 426.74179077	425.26434326, 425.26434326
cx, cy	423.93927002, 233.14932251	425.04409790, 232.82229614
distortion_model	plumb_bob	plumb_bob
D	all zeros (rectified)	all zeros (rectified)
R	identity	identity
frame_id	cam1_depth_optical_frame	cam2_depth_optical_frame

K is stored row-major: [fx, 0, cx, 0, fy, cy, 0, 0, 1].

5.3 extrinsics_depth_to_color (measured identical across all 54 takes per camera)

	cam1	cam2
translation (m)	[0.01506947, −0.00010992, −0.00005541]	[0.01489252, 0.00028989, 0.00014352]
‖translation‖	15.07 mm	14.90 mm
rotation	≈ identity (max off-diagonal 0.0103)	≈ identity (max off-diagonal 0.0056)
layout	column_major	column_major

That 15 mm is the D435's physical baseline between the depth and RGB imagers. layout='column_major' is load-bearing: np.asarray(rot).reshape(3, 3) gives you the transpose of the rotation matrix. Use .reshape(3, 3).T.

5.4 Decoding, and aligning depth to colour

import h5py, cv2, numpy as np

take = "take_01_20260914_164811"
with h5py.File(f"{take}/depth.h5", "r") as f:
    g = f["cam1"]
    t_depth = g["t_rel_s"][:]                       # 30 Hz, same origin as vectors.h5
    png     = np.asarray(g["png"][0], dtype=np.uint8)          # one whole PNG file
    depth_mm = cv2.imdecode(png, cv2.IMREAD_UNCHANGED)         # uint16 (480, 848), mm
    K   = np.array(g["camera_info"].attrs["K"]).reshape(3, 3)  # row-major
    R   = np.array(g["extrinsics_depth_to_color"].attrs["rotation"]).reshape(3, 3).T
    tvec = np.array(g["extrinsics_depth_to_color"].attrs["translation"])   # metres

valid = depth_mm > 0                 # 0 == no return; mask before you do anything
depth_m = depth_mm.astype(np.float32) * 0.001

The recorder also ships a ROS-free reader — read_depth_frame(h5, cam, idx), iter_depth_frames(h5, cam) and depth_meta(h5, cam) in gello_recorder/depth_writer.py — which does exactly the above.

To back-project and move a depth pixel into the colour camera's frame:

fx, fy, cx, cy = K[0, 0], K[1, 1], K[0, 2], K[1, 2]
v, u = np.nonzero(valid)
Z = depth_m[v, u]
P_depth = np.stack([(u - cx) / fx * Z, (v - cy) / fy * Z, Z], axis=1)   # metres
P_color = P_depth @ R.T + tvec                                         # colour optical frame

⚠️ The colour intrinsics are not in this release. depth.h5/<cam>/camera_info describes the 848×480 depth imager only (see its frame_id), so the last step — projecting P_color onto the 1280×720 image — needs the colour K, which was not recorded. Two honest options: read the factory calibration off the same two D435 bodies (serials in §1) with rs-enumerate-devices -c, or treat the 15 mm baseline as negligible for your purpose and use the depth frame as its own coordinate system. Do not assume depth[v, u] is the depth of colour[v, u] — the resolutions differ (848×480 vs 1280×720) and so do the fields of view; the D435 depth FOV is visibly wider, which you can see in assets/color_depth_samples.jpg.

5.5 Measured depth quality

Sampled on one mid-take frame per camera per take (108 decoded frames), plus a second sample on the frame nearest each take's final gripper closure:

	cam1 (scene)	cam2 (wrist)
valid pixels (d > 0), mid frame	85.15 – 88.71 %, median 86.94 %	50.48 – 67.95 %, median 62.32 %
median range of valid pixels, mid frame	0.526 – 0.549 m, median 0.540 m	0.184 – 0.341 m, median 0.207 m
valid pixels at the grasp instant	85.36 – 88.35 %, median 86.91 %	37.13 – 67.50 %, median 61.51 %

⚠️ The wrist camera works below the D435 minimum range. At 848×480 the D435's minimum depth is ~0.2 m, and the measured median range from cam2 is 0.207 m — i.e. the whole scene sits at or under the sensor's floor. The consequence is in the numbers above: a third to a half of every wrist depth frame is invalid, and at the moment of the grasp it gets worse (worst take 37.13 % valid). The carrot and the gripper fingers are frequently holes exactly when you most want them. cam1 has no such problem at 0.54 m. Treat cam2 depth as a sparse, partially reliable cue, not as a depth map. The black regions in the two right-hand columns of assets/color_depth_samples.jpg are these holes.

5.6 Depth frame counts

Depth is an independent stream with its own clock, so it does not match the colour frame counts exactly: totals are 18,541 depth frames against 18,557 colour frames for cam1, and 18,538 against 18,557 for cam2. Per take the difference is −1 or +1 in 24 takes (cam1) and in 29 takes (cam2), plus take_28, where cam2 depth is 2 frames short — 30 cam2 takes differing in total. Align depth to colour by nearest t_rel_s, never by index. Per-take deltas are in §7 and dataset_stats.json.

---

6. Camera timelines and the colour videos

cam1.mp4 / cam2.mp4, 1280×720, 30 fps, MPEG-4, yuv420p — measured identical across all 108 files. Frame k of the video corresponds to row k of cam*_frames, whose frame_idx is the 0-based video frame number and whose t_rel_s is its timestamp.

Measured: MP4 frame counts equal cam*_frames row counts in all 54 takes, both cameras (108/108 videos), verified with ffprobe -count_frames.

The two cameras run on independent clocks: in 24 of 54 takes cam1 and cam2 differ by ±1 frame (12 takes at +1, 12 at −1; no take differs by more than 1). The session totals happen to be equal at 18,557 each, which is a coincidence of the per-take signs cancelling — do not read it as the two cameras being in lockstep. Never assume cam1[k] and cam2[k] are simultaneous.

---

7. Per-take statistics

Row counts per stream, duration, the measured mean command rate, and the sampled depth valid-pixel percentage. Δcam = cam1 rows − cam2 rows; d-cam1 / d-cam2 are depth frame counts. ⚠️ marks the three re-grasp takes (§9).

take	dur (s)	cam1	cam2	Δcam	d-cam1	d-cam2	command	ur_joint_states	tcp_pose	wrench	gripper	gello	cmd Hz	cam1 valid %	cam2 valid %
take_01_20260914_164811	9.36	280	281	-1	280	280	558	558	558	558	369	281	59.8	86.5	53.6
take_02_20260914_164845	10.06	301	302	-1	301	301	695	695	695	695	398	302	69.1	88.0	59.1
take_03_20260914_164930	11.17	335	334	+1	335	334	704	704	704	704	431	336	63.1	85.8	60.7
take_04_20260914_165011	9.53	286	285	+1	285	286	648	649	649	649	369	286	68.1	86.9	66.8
take_05_20260914_165156	10.61	317	318	-1	318	317	718	719	719	718	425	318	67.7	86.8	64.9
take_06_20260914_165238	10.78	323	323	0	323	323	724	723	723	723	422	323	67.2	88.0	63.4
take_07_20260914_165316	9.25	277	277	0	277	276	574	574	574	574	362	277	62.2	86.6	62.1
take_08_20260914_165353	10.96	329	328	+1	328	328	766	765	765	765	432	328	69.9	86.5	64.0
take_09_20260914_165429	8.70	261	260	+1	260	261	584	584	584	584	349	261	67.2	87.6	59.3
take_10_20260914_165510	9.94	298	298	0	297	298	681	682	681	681	380	298	68.6	87.1	65.9
take_11_20260914_165557	12.59	378	377	+1	377	378	885	886	885	885	487	378	70.3	87.1	67.9
take_13_20260914_165647	9.37	280	281	-1	280	281	614	613	613	613	361	281	65.6	86.9	62.4
take_14_20260914_165719	12.35	370	370	0	370	369	822	822	822	822	475	371	66.6	86.5	61.8
⚠️ take_15_20260914_165753	13.92	417	417	0	417	417	956	955	955	955	549	417	68.7	86.8	58.9
take_16_20260914_165825	11.18	335	336	-1	335	335	753	753	753	753	436	336	67.4	87.2	65.1
take_17_20260914_165859	10.03	300	301	-1	301	300	705	704	704	704	388	300	70.4	88.4	65.5
take_18_20260914_165926	11.84	355	355	0	355	354	804	803	803	803	462	355	68.0	85.7	62.4
take_19_20260914_170024	12.38	371	371	0	371	370	849	848	847	847	468	372	68.6	86.0	63.7
take_20_20260914_170107	10.62	318	319	-1	319	318	707	707	707	707	414	319	66.6	85.8	57.0
take_21_20260914_170135	10.06	302	301	+1	301	302	661	661	661	661	394	302	65.8	85.2	62.0
take_22_20260914_170211	12.77	383	383	0	383	382	868	869	868	868	491	383	68.0	88.0	61.2
take_23_20260914_170302	10.76	323	322	+1	322	322	715	716	715	715	422	322	66.5	85.8	62.2
take_24_20260914_170339	8.89	266	266	0	266	266	393	394	393	393	354	266	44.3	87.5	50.5
take_25_20260914_170407	8.86	265	266	-1	265	266	418	419	418	418	361	265	47.2	87.1	67.9
take_26_20260914_170447	10.11	302	303	-1	301	302	497	496	496	496	391	303	49.2	87.3	56.3
take_27_20260914_170519	9.03	271	270	+1	270	270	560	560	560	560	348	271	62.1	87.7	67.3
take_28_20260914_170557	9.71	291	291	0	290	289	400	399	399	399	377	291	41.2	87.1	57.6
take_29_20260914_170629	11.34	339	339	0	339	340	714	714	714	714	438	340	63.0	86.9	65.5
take_30_20260914_170700	11.03	330	331	-1	330	331	678	678	678	678	425	331	61.6	87.4	60.3
take_31_20260914_170732	10.34	310	310	0	310	309	639	638	638	638	395	310	61.8	86.3	62.3
take_32_20260914_170803	9.80	294	293	+1	293	293	601	600	600	600	383	294	61.4	86.6	62.4
take_33_20260914_170854	9.00	270	270	0	270	269	540	539	539	539	354	270	60.0	86.9	60.7
take_34_20260914_170932	12.38	371	371	0	370	371	835	835	835	835	480	371	67.5	88.7	61.5
take_35_20260914_171001	13.34	400	400	0	399	400	927	927	927	927	511	400	69.6	88.5	64.9
take_36_20260914_171037	15.63	469	468	+1	468	468	1059	1059	1058	1059	606	469	67.8	87.7	64.7
take_37_20260914_171113	13.26	397	397	0	397	397	899	898	898	898	502	398	67.8	87.2	62.9
take_38_20260914_171144	10.75	322	322	0	321	322	718	718	718	718	421	323	66.8	87.0	61.5
⚠️ take_39_20260914_171227	13.15	394	394	0	394	393	875	875	875	875	528	394	66.6	87.2	63.4
⚠️ take_40_20260914_171256	12.76	383	382	+1	382	382	851	851	851	851	523	382	66.8	85.3	64.0
take_41_20260914_171328	10.94	328	328	0	328	327	740	740	740	740	423	328	67.8	87.7	62.1
take_42_20260914_171358	11.05	331	331	0	331	331	765	765	765	765	431	331	69.3	87.4	68.0
take_43_20260914_171435	10.54	316	316	0	315	315	703	702	702	702	414	316	66.8	86.1	66.3
take_44_20260914_171505	10.27	307	308	-1	308	307	673	673	673	673	391	308	65.6	86.4	60.6
take_45_20260914_171534	12.96	388	388	0	388	388	877	877	877	877	490	389	67.7	86.9	54.8
take_46_20260914_171603	13.89	416	416	0	416	415	952	952	951	952	526	417	68.6	86.2	63.8
take_47_20260914_171640	12.90	386	386	0	386	386	882	881	880	881	495	387	68.4	88.5	61.6
take_48_20260914_171732	10.17	305	305	0	304	304	651	651	651	651	395	304	64.2	87.3	60.0
take_49_20260914_171812	17.78	533	533	0	533	532	1243	1242	1242	1242	677	533	70.0	86.7	65.6
take_50_20260914_171849	15.12	453	453	0	453	453	1055	1056	1056	1056	574	453	69.9	86.0	55.2
take_51_20260914_171924	14.61	438	438	0	438	437	1019	1018	1018	1018	549	438	69.8	87.8	66.6
take_52_20260914_171957	14.87	446	446	0	445	446	1021	1021	1021	1021	560	446	68.7	87.0	52.2
take_53_20260914_172033	10.42	312	312	0	312	312	709	708	708	708	401	313	68.1	85.7	57.1
take_54_20260914_172116	16.97	509	508	+1	508	509	1126	1125	1125	1125	641	509	66.4	88.0	65.6
take_55_20260914_172155	9.23	276	277	-1	276	276	629	628	628	628	367	277	68.2	85.8	63.7
TOTAL	619.33	18,557	18,557	—	18,541	18,538	40,640	40,629	40,619	40,621	24,015	18,573	—	—	—

Duration: median 10.86 s, min 8.70 s (take_09), max 17.78 s (take_49).

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8. How to load (h5py)

import json, h5py, numpy as np

path = "take_03_20260914_164930/vectors.h5"
with h5py.File(path, "r") as f:
    # correct way to read the column list (do NOT list() the raw attr string):
    cols = json.loads(f["ur_joint_states"].attrs["columns"])   # -> ['t_rel_s','q1',...]

    # measured UR7e joint positions (N_ur, 6), radians, on the UR clock (~67 Hz)
    # NOTE: this clock is stamped 0.900 s LATE -- subtract it before aligning (§3.6)
    ur_t = f["ur_joint_states"]["t_rel_s"][:] - 0.900
    ur_q = np.stack([f["ur_joint_states"][f"q{i}"][:] for i in range(1, 7)], axis=1)

    # commanded joint targets = the action (N_cmd, 6), ~67 Hz
    cmd_t = f["command"]["t_rel_s"][:]
    cmd   = np.stack([f["command"][f"cmd{i}"][:] for i in range(1, 7)], axis=1)

    # camera master timeline (30 Hz); frame_idx maps into cam1.mp4
    cam1_t   = f["cam1_frames"]["t_rel_s"][:]
    cam1_idx = f["cam1_frames"]["frame_idx"][:].astype(int)

# streams are at DIFFERENT rates and arrive in bursts — align to the camera grid
# by nearest timestamp, never by index:
def nearest_idx(src_t, query_t):
    j = np.clip(np.searchsorted(src_t, query_t), 1, len(src_t) - 1)
    left, right = src_t[j - 1], src_t[j]
    return np.where(query_t - left <= right - query_t, j - 1, j)

ur_on_cam = ur_q[nearest_idx(ur_t, cam1_t)]   # (N_frames, 6) aligned to video

Read a specific video frame (OpenCV): cv2.VideoCapture("cam1.mp4") then read sequentially; frame k corresponds to cam1_frames/frame_idx[k]. Because cam1 and cam2 differ by ±1 frame in 24 of 54 takes, map between the two cameras by nearest timestamp too — and likewise for depth (§5.6).

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9. Anomalies & data-quality notes

• ⚠️ Timestamp artefact — ur_joint_states, tcp_pose and wrench rows are stamped late (§3.6). ur_joint_states by 0.900 s (per-take optimum 0.895–0.900 s, median 0.900; 0.89–0.91 s depending on estimator and grid), tcp_pose and wrench by ≈0.41 s (= 0.900 − 0.495, the tightest measured quantity here). It is a recorder artefact — a single spin thread, t_rel_s = callback execution time, and KEEP_LAST queues of depth 100/50 kept permanently full once depth recording lowered the service rate to ≈67 Hz (41–70 Hz) — not robot or controller lag. command, both cameras, both depth streams, gello_joint_states and gripper are fresh (depth header-stamp age 0.022 s). It is a pure delay: subtract 0.900 s from ur_joint_states/t_rel_s and ≈0.41 s from tcp_pose/wrench, and shift nothing else. The files here are as recorded; the derived LeRobot release has the ur_joint_states correction already applied and drops the last ~0.9 s of every take (1,469 frames, leaving 54 episodes / 17,088 frames) because the shift leaves it with no joint sample; the recorder was fixed after this release.

• Take-number gap {12} is by design. Folders run take_01 … take_55 but take_12 was discarded during recording, giving exactly 54 folders. It is not missing data and there is no partial take_12 anywhere in the release.

• Three takes contain a missed first grasp and a re-grasp. They are the only three takes with more than one grip_cmd ≥ 0.7 closure. Measured:

  take	1st close	released	grip_pos peak during 1st close	2nd close	released	grip_pos peak
  take_15_20260914_165753	4.20 s	4.61 s	0.4314	6.67 s	11.61 s	0.6078
  take_39_20260914_171227	3.39 s	4.45 s	0.8980	6.65 s	10.59 s	0.5451
  take_40_20260914_171256	2.88 s	3.80 s	0.8980	6.81 s	10.35 s	0.5137

  take_39 and take_40 are true closures on nothing: the fingers reach 0.8980, the empty-gripper stop, and are then reopened. take_15 is different and the distinction is worth keeping — its first closure was aborted after 0.41 s, with grip_pos only reaching 0.4314 (less closed than the 0.6078 it later reaches on the carrot), so the fingers never met. All three then re-approach and grasp successfully. All three are included in both the raw and the LeRobot releases: a recovery is a legitimate demonstration, not a defect. Full traces are in dataset_stats.json → regrasp_takes and per_take[*].grip_closures.

• The leader stream is not a follower mirror (§3.4) — the single biggest behavioural difference from cube_in_cup_raw, and a direct consequence of this session running in EEF delta mode rather than joint mode (§1).

• cam1 / cam2 frame-count mismatch: 24 of 54 takes differ by exactly ±1 (§6).

• Depth / colour frame-count mismatch: 24 takes (cam1) and 29 takes (cam2) differ by ±1, plus take_28 cam2 at −2 — 30 cam2 takes differing in total (§5.6).

• Wrist depth is below the sensor's minimum range (§5.5) — 37–68 % valid.

• Four takes log the robot streams at 41–49 Hz instead of ~67 Hz: take_24, take_25, take_26, take_28 (§3.1).

• synchronized/ is empty in all 54 takes (§3.5).

• columns attribute is a JSON string, not a list — always json.loads it (§3.2).

• Bursty sampling on the robot streams (bimodal Δt, §3.1) — resample by timestamp.

• No NaN and no Inf anywhere: measured over every channel of every group of all 54 takes.

• Video ↔ HDF5 agreement is exact: all 54 takes, both cameras (108/108 videos), verified with ffprobe -count_frames.

• Timing: largest single gap in any robot stream is 44.1 ms; in any colour camera stream 61.0 ms (~2 frame periods); in any depth stream 69.2 ms; in gripper 69.4 ms; in gello_joint_states 69.6 ms.

• Uniform schema: identical 9 groups, identical channel names, all float64, in all 54 vectors.h5; identical depth camera_info and extrinsics_depth_to_color in all 54 depth.h5, per camera.

• Cleanliness: 0 stray files, 0 hidden files, no sub-directories in the take folders, and no /home/ absolute-path string anywhere in the 216 data files (54 vectors.h5 + 54 depth.h5 + 108 MP4s, verified by full byte scan).

Every take ends with the carrot in the pot (operator-reported, 54/54). That is the only outcome label available; there are no per-step success/failure labels, no human quality ratings, and no policy-performance numbers in this release.

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10. Reproducing these numbers

python3 make_carrot_raw_stats.py --data /path/to/Put_carrot_in_pot

(system python3 with h5py, numpy, cv2; ffprobe on PATH for exact frame counts — the script records which method it used in aggregate.video_frame_count_method). It reads the take folders read-only and writes dataset_stats.json.

See dataset_stats.json for exact per-take frame counts, durations, row counts, byte sizes, rate statistics, depth quality samples, leader/follower statistics, per-channel min/max/mean/std, and — under timestamp_lag — the per-take timestamp offsets of §3.6 with both residual definitions and the cross-correlation cross-checks.