# Data Dictionary — `Bigenlight/cube_in_cup_raw`

Raw teleoperation recordings for the task **"put the cube in the cup"**, captured via a
**GELLO leader → UR7e follower** setup with two RGB cameras, in a single session on **2026-07-20**.

This document describes the **RAW** release: the original per-take HDF5 signal logs plus the
original camera MP4s, exactly as recorded. For a ready-to-train version see the LeRobot dataset
[`cube_in_cup_lerobot_v3`](https://huggingface.co/datasets/Bigenlight/cube_in_cup_lerobot_v3).

- **Scale:** 24 takes (`take_*`) · 6,226 cam1 frames (6,231 cam2) · 207.6 s (~3.5 min) · ~219 MB
- **Every take folder** `take_NN_YYYYMMDD_HHMMSS/` contains exactly three files:
  `cam1.mp4`, `cam2.mp4`, `vectors.h5`. No hidden or stray files; no sub-directories.
- Verified read-only against all 24 H5 files and all 48 MP4s; **no absolute-path leakage** and no
  PII inside the H5.
- Every figure in this document was measured from the files themselves. The machine-readable
  version is [`dataset_stats.json`](dataset_stats.json).

---

## 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. Operator moves GELLO; its joint positions are mapped to UR7e joint targets. Recorded `gello_*` streams are the **leader** signal — kept for completeness but **NOT observable at inference** (the robot cannot see the leader). Do not use `gello_*` as an input feature. |
| **Gripper** | **Robotiq 2F-85** two-finger parallel gripper. Continuous normalized command (`grip_cmd`) plus a continuous measured position (`grip_pos`), and the leader-side trigger `gello_grip`. |
| **Camera 1** | **Intel RealSense D435** — RGB only. |
| **Camera 2** | **Intel RealSense D435if** (a D435 variant) — RGB only. |
| **Video format** | 1280×720 (720p), **30 fps**, MPEG-4 (`mpeg4`), color `yuv420p`. Verified identical across all 48 videos. **No depth / no IR** recorded despite RealSense capability — color stream only. The LeRobot copy re-encodes to AV1; RAW keeps MPEG-4. |
| **Viewpoints** | Two fixed viewpoints of **different kinds**: one RealSense **wrist-mounted** just above the gripper (eye-in-hand close-up), one on a **tripod** beside the table (third-person scene view). **This release does not assert which is `cam1` and which is `cam2`** — that mapping was not verified. The `cam1` ↔ `cam2` order is meaningful and must be preserved at deploy time. |
| **Fiducial** | An ArUco/AprilTag marker is present on the **robot base plate**, not on the work surface. |
| **Scene** | A light wood-grain tabletop holding exactly two objects: a **purple/lavender wooden cube** (~5 cm) and a **sage-green tapered plastic cup**. **No distractor objects.** Success = cube placed in the cup. |

---

## 2. HDF5 file (`vectors.h5`) — top-level layout

One file per take. Root has **9 groups** (schema identical in all 24 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 (starts at 0.0). 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 24 takes) | Native rate | What it is |
|---|--:|---|---|
| `cam1_frames` | 6,226 | 30.0 Hz | Timestamp + frame index for each Camera-1 video frame |
| `cam2_frames` | 6,231 | 30.0 Hz | Timestamp + frame index for each Camera-2 video frame |
| `command` | 20,371 | **~98 Hz** | Commanded UR7e joint targets (the **action**), radians |
| `ur_joint_states` | 19,968 | **~97 Hz** | Measured UR7e joint state: position, velocity, effort |
| `tcp_pose` | 19,964 | **~97 Hz** | Measured tool-center-point pose (position + quaternion) |
| `wrench` | 19,965 | **~97 Hz** | 6-axis force/torque at the TCP |
| `gripper` | 7,575 | **~36.5 Hz** | Gripper command, measured position, leader trigger |
| `gello_joint_states` | 6,240 | 30.0 Hz | GELLO **leader** joint pos + vel (teleop only — not for inference) |
| `synchronized` | **0 (EMPTY)** | — | Intended fused/aligned table; **empty in all 24 takes** — ignore |

### Measured rates (all 24 takes)

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` | 29.97 | 29.93 – 30.02 | 33.2 | 28.1 | 53.8 | 30.1 |
| `cam2_frames` | 30.00 | 29.94 – 30.11 | 33.2 | 28.8 | 65.1 | 30.1 |
| `command` | **97.95** | 86.87 – 117.72 | 13.2 | 2.9 | 42.2 | 75.8 |
| `ur_joint_states` | **97.03** | 86.93 – 99.93 | 13.4 | 2.9 | 43.7 | 74.6 |
| `tcp_pose` | **97.05** | 86.97 – 99.94 | 13.5 | 2.9 | 44.1 | 74.1 |
| `wrench` | **97.05** | 86.96 – 99.94 | 13.4 | 2.9 | 43.6 | 74.6 |
| `gripper` | **36.47** | 34.91 – 37.69 | 31.9 | 2.1 | 62.0 | 31.4 |
| `gello_joint_states` | 30.01 | 29.95 – 30.07 | 33.2 | 22.8 | 62.6 | 30.1 |

*Mean rate* = `(N−1)/(t_last − t_first)` per take, 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** — roughly 5 % of intervals
> are ~2.9 ms (samples arriving back-to-back) while the median is ~13.3 ms. Averaging over the
> whole take therefore gives ~98 Hz, while the typical spacing corresponds to ~75 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.**

> **Comparison with the sibling dataset.** These robot streams run at ~97–98 Hz, versus ~56–60 Hz
> in [`banana_in_pot_raw`](https://huggingface.co/datasets/Bigenlight/banana_in_pot_raw) — about
> **3.2 robot samples per camera frame** here against ~2 there. Same rig, faster logging session.

### ⚠️ The `columns` attribute quirk (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. Verified: h5py returns Python `str` in
all 24 files. If a naive consumer does `list(grp.attrs["columns"])` expecting a list, it
**iterates the string character-by-character** and you get `['[', '"', 't', '_', 'r', ...]` — the
"garbled char-by-char" failure. **Always `json.loads(grp.attrs["columns"])`.** The correct
per-group column lists are given verbatim below and do not depend on the attribute.

---

## 3. Per-group / per-channel schema

All datasets `float64`, shape `(N,)` where `N` = that group's row count for the take. Ranges are
**measured across all 24 takes** (take_23 included).

### `cam1_frames` — Camera-1 frame timeline
| Channel | dtype | Unit | Meaning | Range |
|---|---|---|---|---|
| `t_rel_s` | float64 | s | Time of this frame, since take start | 0 → 16.82 |
| `frame_idx` | float64 | index | 0-based frame number in `cam1.mp4` (float-typed) | 0 → 504 |

### `cam2_frames` — Camera-2 frame timeline
| Channel | dtype | Unit | Meaning | Range |
|---|---|---|---|---|
| `t_rel_s` | float64 | s | Time of this frame, since take start | 0 → 16.80 |
| `frame_idx` | float64 | index | 0-based frame number in `cam2.mp4` (float-typed) | 0 → 503 |

### `command` — commanded UR7e joint targets ➜ **the ACTION**
| Channel | dtype | Unit | Meaning | min | max | mean |
|---|---|---|---|--:|--:|--:|
| `t_rel_s` | float64 | s | Timestamp | — | — | — |
| `cmd1` | float64 | rad | Target angle, joint 1 (base) | 2.6016 | 3.2395 | 2.8682 |
| `cmd2` | float64 | rad | Target angle, joint 2 (shoulder) | −1.7333 | −1.1296 | −1.4453 |
| `cmd3` | float64 | rad | Target angle, joint 3 (elbow) | 1.4592 | 2.3129 | 1.9785 |
| `cmd4` | float64 | rad | Target angle, joint 4 (wrist 1) | −2.6559 | −1.5630 | −2.2316 |
| `cmd5` | float64 | rad | Target angle, joint 5 (wrist 2) | −1.7499 | −1.3692 | −1.5019 |
| `cmd6` | float64 | rad | Target angle, joint 6 (wrist 3) | −3.7518 | −3.0308 | −3.4761 |

### `ur_joint_states` — measured UR7e joint state ➜ core of **observation.state**
| Channel | dtype | Unit | Meaning | min | max | mean |
|---|---|---|---|--:|--:|--:|
| `t_rel_s` | float64 | s | Timestamp | — | — | — |
| `q1` | float64 | rad | Measured angle, joint 1 | 2.6016 | 3.2402 | 2.8606 |
| `q2` | float64 | rad | Measured angle, joint 2 | −1.7067 | −1.1299 | −1.4411 |
| `q3` | float64 | rad | Measured angle, joint 3 | 1.4553 | 2.3131 | 1.9832 |
| `q4` | float64 | rad | Measured angle, joint 4 | −2.6559 | −1.5613 | −2.2401 |
| `q5` | float64 | rad | Measured angle, joint 5 | −1.7499 | −1.3693 | −1.5022 |
| `q6` | float64 | rad | Measured angle, joint 6 | −3.7516 | −3.0714 | −3.4827 |
| `qd1` | float64 | rad·s⁻¹ | Measured velocity, joint 1 | −0.6425 | 0.5499 | −0.0186 |
| `qd2` | float64 | rad·s⁻¹ | Measured velocity, joint 2 | −0.6479 | 0.5250 | −0.0019 |
| `qd3` | float64 | rad·s⁻¹ | Measured velocity, joint 3 | −0.6256 | 0.4089 | 0.0402 |
| `qd4` | float64 | rad·s⁻¹ | Measured velocity, joint 4 | −0.5344 | 0.6628 | −0.0609 |
| `qd5` | float64 | rad·s⁻¹ | Measured velocity, joint 5 | −0.2336 | 0.6557 | 0.0070 |
| `qd6` | float64 | rad·s⁻¹ | Measured velocity, joint 6 | −0.6309 | 0.6635 | −0.0340 |
| `eff1` | float64 | N·m (motor effort / current proxy) | Effort, joint 1 | −3.8302 | 3.2727 | −0.2260 |
| `eff2` | float64 | N·m | Effort, joint 2 | −7.0836 | 3.2122 | −2.1933 |
| `eff3` | float64 | N·m | Effort, joint 3 | −4.5974 | 5.8018 | −1.4677 |
| `eff4` | float64 | N·m | Effort, joint 4 | −0.8045 | 1.9341 | −0.4613 |
| `eff5` | float64 | N·m | Effort, joint 5 | −0.9306 | 0.8294 | 0.0757 |
| `eff6` | float64 | N·m | Effort, joint 6 | −0.5034 | 0.5093 | −0.0513 |

### `tcp_pose` — measured tool-center-point pose (base frame)
| Channel | dtype | Unit | Meaning | min | max | mean |
|---|---|---|---|--:|--:|--:|
| `t_rel_s` | float64 | s | Timestamp | — | — | — |
| `x` | float64 | m | TCP position x in robot base frame | 0.4199 | 0.5975 | 0.5090 |
| `y` | float64 | m | TCP position y | −0.1456 | 0.1888 | −0.0019 |
| `z` | float64 | m | TCP position z | 0.1785 | 0.4873 | 0.2971 |
| `qx` | float64 | unit quaternion | Orientation x | −0.7475 | 0.7919 | 0.2863 |
| `qy` | float64 | unit quaternion | Orientation y | −0.7860 | 0.8321 | 0.3238 |
| `qz` | float64 | unit quaternion | Orientation z | −0.1070 | 0.1458 | 0.0444 |
| `qw` | float64 | unit quaternion | Orientation w | 0.0000 | 0.1904 | 0.0571 |

Workspace extent: **~18 cm in x, ~33 cm in y, ~31 cm in z** — a compact tabletop envelope.
Note `qw` stays near 0 while `qx`/`qy` dominate: the tool points essentially straight down
throughout, as expected for a top-down pick-and-place.

### `wrench` — 6-axis force/torque at TCP
| Channel | dtype | Unit | Meaning | min | max | mean |
|---|---|---|---|--:|--:|--:|
| `t_rel_s` | float64 | s | Timestamp | — | — | — |
| `fx` | float64 | N | Force along x | −17.544 | 63.420 | 1.849 |
| `fy` | float64 | N | Force along y | −17.387 | 58.453 | −1.510 |
| `fz` | float64 | N | Force along z | −143.191 | 5.015 | −2.095 |
| `tx` | float64 | N·m | Torque about x | −6.882 | 2.026 | 0.518 |
| `ty` | float64 | N·m | Torque about y | −3.852 | 3.509 | −0.394 |
| `tz` | float64 | N·m | Torque about z | −1.996 | 0.706 | 0.142 |

The wide `fz` excursion (down to −143 N) is a transient contact/acceleration spike; the mean is
−2.1 N with a standard deviation of 9.3 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 | 0.3787 |
| `grip_cmd` | float64 | normalized 0–1 (open→close) | Commanded gripper (part of the **action**) | 0.0000 | 0.9998 | 0.3788 |
| `grip_pos` | float64 | normalized | Measured gripper opening (part of **observation.state**) | 0.0118 | 0.6314 | 0.2183 |

`grip_pos` never reaches 1.0 — 0.0118 is the closed extreme and 0.6314 the widest observed opening
(in `take_09`; typical open is ~0.50). That is the physical range the fingers swept, not clipping.
`grip_cmd` and `gello_grip` track each other almost exactly (means 0.3788 vs 0.3787).

### `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 | 2.6001 | 3.2413 |
| `q2` | float64 | rad | Leader joint 2 angle | −1.7489 | −1.1277 |
| `q3` | float64 | rad | Leader joint 3 angle | 1.4707 | 2.3175 |
| `q4` | float64 | rad | Leader joint 4 angle | −2.6611 | −1.5673 |
| `q5` | float64 | rad | Leader joint 5 angle | −1.7514 | −1.3679 |
| `q6` | float64 | rad | Leader joint 6 angle ⚠ **+2π offset** | **2.5307** | **3.2578** |
| `qd1`…`qd6` | float64 | rad·s⁻¹ | Leader joint velocities 1–6 | — | — |

> ⚠️ **`gello_q6` is wrapped +2π relative to `ur_q6`.** Joints 1–5 track the follower closely —
> the mean of `gello_qi − ur_qi` (nearest-timestamp aligned, pooled over all 24 takes) is within
> **±0.011 rad** for i = 1…5. For **joint 6 it is +6.2797 rad ≈ +2π**, so `gello_q6` lives in
> **[2.53, 3.26]** while `ur_q6` lives in **[−3.75, −3.07]**. The two describe the same physical
> wrist angle in different revolutions. Subtract 2π from `gello_q6` before comparing it to the
> follower or you will read it as a huge tracking error. (This is another reason not to feed
> `gello_*` to a policy — but it matters if you study the leader/follower relationship.)

### `synchronized` — **EMPTY in all 24 takes** (do not use)

The group exists and declares **56 channels**, but **every dataset has shape `(0,)`** in every take.
Fusion was 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
```

---

## 4. Per-take statistics

Row counts per stream, duration, and the measured mean `command` rate. `Δcam` = cam1 rows − cam2
rows. Every take's video frame counts match its `cam*_frames` row counts exactly, both cameras.

| take | dur (s) | cam1 | cam2 | Δcam | `command` | `ur_joint_states` | `tcp_pose` | `wrench` | `gripper` | `gello` | cmd Hz |
|---|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|
| `take_01_20260720_205207` | 16.82 | 505 | 504 | +1 | 1718 | 1662 | 1661 | 1662 | 608 | 505 | 102.2 |
| `take_02_20260720_205410` | 16.41 | 492 | 493 | −1 | 1617 | 1617 | 1617 | 1617 | 588 | 493 | 98.4 |
| `take_03_20260720_205457` | 10.75 | 323 | 323 | 0 | 1076 | 1075 | 1075 | 1075 | 391 | 323 | 99.9 |
| `take_05_20260720_205546` | 9.11 | 273 | 274 | −1 | 899 | 867 | 866 | 866 | 334 | 274 | 98.5 |
| `take_07_20260720_205632` | 8.66 | 260 | 261 | −1 | 807 | 804 | 803 | 803 | 319 | 260 | 93.0 |
| `take_08_20260720_205653` | 8.23 | 247 | 248 | −1 | 821 | 820 | 820 | 820 | 303 | 248 | 99.5 |
| `take_09_20260720_205713` | 11.36 | 341 | 341 | 0 | 1107 | 1107 | 1106 | 1106 | 421 | 342 | 97.5 |
| `take_10_20260720_205742` | 6.88 | 206 | 207 | −1 | 648 | 647 | 647 | 647 | 251 | 208 | 93.8 |
| `take_11_20260720_205805` | 7.56 | 227 | 226 | +1 | 690 | 690 | 690 | 690 | 274 | 227 | 91.4 |
| `take_12_20260720_205835` | 7.36 | 221 | 220 | +1 | 669 | 669 | 669 | 669 | 270 | 221 | 91.1 |
| `take_13_20260720_205855` | 8.29 | 249 | 248 | +1 | 883 | 816 | 816 | 816 | 304 | 249 | 106.4 |
| `take_14_20260720_205919` | 6.97 | 209 | 210 | −1 | 606 | 607 | 607 | 607 | 250 | 209 | 86.9 |
| `take_15_20260720_210005` | 8.45 | 253 | 254 | −1 | 840 | 833 | 833 | 833 | 310 | 255 | 99.1 |
| `take_16_20260720_210041` | 8.67 | 260 | 260 | 0 | 838 | 831 | 831 | 831 | 314 | 260 | 96.8 |
| `take_17_20260720_210110` | 8.45 | 253 | 253 | 0 | 779 | 773 | 773 | 773 | 308 | 254 | 92.1 |
| `take_18_20260720_210129` | 7.15 | 214 | 215 | −1 | 845 | 717 | 717 | 717 | 271 | 216 | 117.7 |
| `take_19_20260720_210149` | 6.82 | 204 | 204 | 0 | 683 | 679 | 679 | 679 | 250 | 204 | 100.2 |
| `take_20_20260720_210211` | 8.09 | 242 | 243 | −1 | 849 | 789 | 789 | 789 | 295 | 244 | 104.6 |
| `take_21_20260720_210234` | 7.71 | 232 | 230 | **+2** | 727 | 727 | 727 | 727 | 281 | 231 | 94.5 |
| `take_22_20260720_210255` | 7.37 | 221 | 221 | 0 | 743 | 723 | 723 | 723 | 270 | 222 | 100.7 |
| ⚠️ `take_23_20260720_210316` | **1.64** | 49 | 50 | −1 | 170 | 162 | 162 | 162 | 57 | 50 | 103.0 |
| `take_24_20260720_210319` | 8.24 | 247 | 247 | 0 | 776 | 775 | 775 | 775 | 300 | 247 | 94.2 |
| `take_25_20260720_210344` | 8.96 | 269 | 269 | 0 | 868 | 866 | 866 | 866 | 324 | 268 | 97.0 |
| `take_26_20260720_210403` | 7.63 | 229 | 230 | −1 | 712 | 712 | 712 | 712 | 282 | 230 | 93.3 |
| **TOTAL** | **207.58** | **6226** | **6231** | — | **20371** | **19968** | **19964** | **19965** | **7575** | **6240** | — |

Duration: median **8.24 s**, min 1.64 s (`take_23`, the misfire), max 16.82 s (`take_01`).

---

## 5. How to load (h5py)

```python
import json, h5py, numpy as np

path = "Put_cube_in_cup/take_03_20260720_205457/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 (~97 Hz)
    ur_t = f["ur_joint_states"]["t_rel_s"][:]
    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), ~98 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
16 of 24 takes, map between the two cameras by nearest timestamp too.

## 6. How to convert to LeRobot

Use `convert_to_lerobot.py` — the same recipe as the banana family. It resamples every stream onto
the **cam1 timestamp grid** at **30 fps** via nearest-timestamp lookup and produces:

- `observation.state` (7) = `ur_joint_states q1..q6` + `gripper/grip_pos`
- `action` (7) = `command cmd1..cmd6` + `gripper/grip_cmd`
- `observation.images.cam1`, `observation.images.cam2` (720×1280×3 RGB video, re-encoded to AV1)
- `gello_*` streams are intentionally dropped (not observable at inference).
- **`take_23` is excluded**, giving 23 episodes from 24 raw takes.

```bash
lr_env/bin/python convert_to_lerobot.py --data Put_cube_in_cup \
    --out cube_in_cup_lerobot --repo-id Bigenlight/cube_in_cup_lerobot_v3
```

---

## 7. Anomalies & data-quality notes

- **⚠️ `take_23_20260720_210316` is a misfire.** Duration 1.64 s (49 cam1 frames); the UR is
  essentially motionless (per-joint range **< 0.013 rad**); the gripper is **never actuated**
  (`grip_pos` flat at 0.0118, `grip_cmd` flat at 0.0, total range 0.0). It contains no
  demonstration of the task. It is **included in this raw release** (raw = as recorded) and
  **excluded from `cube_in_cup_lerobot_v3`**. Drop it in any pipeline built from these files.
- **Take-number gaps `{4, 6}` are by design.** Folder names span `take_01`…`take_26`; takes 04 and
  06 were aborted/discarded during recording, leaving exactly **24** folders. Not missing data.
- **Duration outliers (not defects):** `take_01` (16.82 s) and `take_02` (16.41 s) run about **2×
  the median** take length (8.24 s) — early, slower demonstrations. Their signals are clean.
- **Camera frame-count mismatch:** in **16 of 24** takes `cam1` and `cam2` differ by **±1 frame**,
  except `take_21` at **+2** (totals: 6,226 cam1 vs 6,231 cam2). Per-take deltas are in §4. Handle
  by nearest-timestamp mapping between the two camera clocks — **never assume `cam1[k]` and
  `cam2[k]` are simultaneous.**
- **`synchronized/` empty** in all 24 takes (see §3).
- **`columns` attribute is a JSON string, not a list** — always `json.loads` it (see §2).
- **Bursty sampling** on the robot streams (bimodal Δt, see §2) — resample by timestamp.
- **No NaN and no Inf** anywhere: every channel of every group of all 24 takes is finite.
- **Video ↔ HDF5 agreement is exact:** all 24 takes, both cameras (48/48 videos), have MP4 frame
  counts equal to their `cam*_frames` row counts (verified with `ffprobe -count_frames`).
- **Timing:** largest single gap in any robot stream is **44 ms**; largest in any camera stream is
  **65 ms** (~2 frame periods). Max-to-median interval ratio is 1.2–3.2× per take.
- **Joint safety envelope:** all takes stay within it (see §3 for measured per-joint ranges).
- **Uniform schema:** identical 9 groups, identical channel names, all `float64`, in all 24 files.
- Cleanliness: 0 stray files, 0 hidden files, no sub-directories in the take folders, and no
  `/home/` absolute-path strings inside any of the 24 H5 files.

No success/failure labels or human quality ratings ship with this dataset, and **no trained policy
exists for this task yet**.

See [`dataset_stats.json`](dataset_stats.json) for exact per-take frame counts, durations, row
counts, byte sizes, rate statistics, and per-channel min/max/mean/std.