# Data Dictionary — `Bigenlight/carrot_in_pot_sim_raw`

Raw **simulated** teleoperation recordings for the task **"Put carrot in pot"**, captured with a
**physical GELLO leader → MuJoCo UR7e follower** and two simulated cameras, in a single session on
**2026-09-15** (00:09–00:19 local).

This document describes the **RAW sim** release: the per-take HDF5 signal logs, the camera MP4s,
the exact MJCF scene and the complete per-tick simulator state, exactly as recorded. It is the
simulated twin of [`carrot_in_pot_raw`](https://huggingface.co/datasets/Bigenlight/carrot_in_pot_raw)
and deliberately uses the **same recorder and the same file format**. For a ready-to-train version
see [`carrot_in_pot_sim_lerobot_v3`](https://huggingface.co/datasets/Bigenlight/carrot_in_pot_sim_lerobot_v3).

- **Scale:** 22 takes (`take_*`), shipped twice — `takes/` (live capture, **9,173 cam1 frames, 9,173 cam2**)
  and `retimed_30hz/` (videos re-rendered on an exact 30 Hz grid, **10,725 cam1, 10,725 cam2**) ·
  **359.13 s (6.0 min)** · **1.32 GB** total.
- **Every take folder contains exactly three files:** `vectors.h5`, `cam1.mp4`, `cam2.mp4`.
  Measured: no hidden or stray files, no sub-directories, in any of the 44 folders. **There is no
  `depth.h5`** — depth recording is opt-in in `sim_collect` and was off for this session.
- Verified read-only against all 44 `vectors.h5` and all 88 MP4s. The only absolute path in the
  release is `sim_meta.retimed.from` in each `retimed_30hz/*/vectors.h5` (source-take provenance);
  `takes/` has none. No PII.
- Every figure here was measured from the files by `make_carrot_raw_stats.py` (depth-free mode).
  Machine-readable: [`dataset_stats.json`](dataset_stats.json) and
  [`retimed_30hz/dataset_stats.json`](retimed_30hz/dataset_stats.json).
- ⚠️ **Read §3.6 before you align streams.** Unlike the real release, **nothing here is stamped
  late** and no constant must be subtracted. Applying the real release's −0.900 s / −0.41 s fix to
  these files would *create* a misalignment.

---

## 1. Simulation & recording setup

| Component | Spec |
|---|---|
| **Simulator** | MuJoCo **3.10.0**, timestep **0.002 s** (500 Hz), integrator `implicitfast`, elliptic friction cone, `impratio` 10, `noslip_iterations` 0, arm gravity compensation 1.0 |
| **Robot (follower)** | **Simulated UR7e**: `mujoco_menagerie` `ur5e.xml` structure with the exact UR7e URDF link offsets (shoulder z 0.1625, wrist_1 z 0.3922, wrist_2 y 0.1263, wrist_3 z 0.0997, attachment_site y 0.0996, elbow ±3.14159). Kinematics DH `d = [0.1625, 0, 0, 0.1333, 0.0997, 0.0996]`, `a = [0, −0.425, −0.3922, 0, 0, 0]`. `attachment_site` world pose == `ur_kin.fk(q)` to **0.000 mm** over 2,000 random q. Visual geometry is the UR5e enclosure. |
| **Gripper** | `mujoco_menagerie` **Robotiq 2F-85** attached at `attachment_site`; driver joint `robotiq_2f85/right_driver_joint`, range 0 – 0.871 rad, normalized **0.0 = open, 1.0 = closed** |
| **Teleoperation (leader)** | The **physical GELLO** arm over USB (XL330 ×7, 57600 baud), sampled at 30 Hz, calibrated from the real robot's ROS file `ur_gello_bringup/config/ur7e_gello.yaml` (`gello_publisher`). Torque is never enabled. |
| **Control mode** | **EEF delta**, the real ROS bridge code path: One-Euro filter bank (`dt = 1/250`, `min_cutoff 1.0`, `beta 2.0`) → `EefDeltaController.step` at **250 Hz** with `pos_scale 1.0`, `r_align_rpy [0,0,0]`, `tool_l = tool_r = 0.174 m`, `v_max 0.16 m/s`, `w_max 1.0 rad/s`, `max_step_rad 0.0025`, `soft_start_s 0.7`, analytic IK. `command` = absolute UR joint targets from IK on a leader **pose** delta. |
| **Gripper mode** | `continuous`, deadband 0.02 (not the 0.3/0.7 discrete latch) |
| **Scene** | No table geometry — the workspace **is** the floor: one 5 m textured plane at z = 0, robot base at the world origin, arm facing world **+x** at home (`home_joints = [−3.302, −1.563, 1.607, −1.523, −1.615, −3.118]`) |
| **Floor texture** | LIBERO `seamless_wood_planks_floor.png` (MIT, © 2023 Lifelong Robot Learning), `texrepeat` 20×20, friction `[1.0, 0.005, 0.0001]` |
| **Objects** | `carrot` (procedural MJCF, food, ~18.5 cm, 0.08 kg) at **+y**, `pot` (procedural MJCF, container, inner radius 0.09 m, rim z 0.11 m, floor z 0.006 m, 0.55 kg) at **−y** |
| **Layout randomization** | carrot nominal `(0.45, +0.18, 0)` ±0.06 m, yaw ±35°; pot nominal `(0.45, −0.22, 0)` ±0.05 m, yaw ±180°; drop height 0.02 m, settle 0.5 s, min gap 0.02 m, base keep-out radius 0.22 m |
| **Camera 1 — scene** | Fixed camera at `(0.70, 0.00, 0.571)` m, `lookat (0.45, 0, 0)`, up `+z`, colour fovy **42°** → rendered intrinsics fx = fy = **937.83**, cx 640, cy 360 at 1280×720 |
| **Camera 2 — wrist** | Attached to `wrist_3_link` relative to `attachment_site`: radial 0.05 m along tool **+y**, axial 0.08 m, pitch **15°** toward the fingertips; same fovy and resolution |
| **Video format** | 1280×720, **30 fps**, `mpeg4`, `yuv420p` — measured identical across all 88 videos (`ffprobe`: `mpeg4 1280 720 yuv420p 30/1`) |
| **Renderer** | MuJoCo offscreen, `MUJOCO_GL=glfw`, **software GL** (no NVIDIA driver on the recording machine) — this is why the live capture rate is below 30 fps (§3.1, §9) |
| **Recording software** | `sim_collect` at git commit `4bac8657be5a66adca0b5ccfacc11982420080a1`, writing through the **real** `gello_recorder.RecordingSession` |

`cam1` shows the pot, the carrot and the arm entering from the right; `cam2` shows the gripper
fingers at the bottom of every frame — the same convention as the real release. See
`assets/take01_cam1_first_frame.jpg`, `assets/take01_cam2_first_frame.jpg` and
`assets/first_frames.jpg`.

---

## 2. Files in a take folder

| File | What it is | Total bytes (`takes/`, 22) | Total bytes (`retimed_30hz/`, 22) |
|---|---|--:|--:|
| `vectors.h5` | all robot/teleop/gripper/camera-timeline signals + the simulator groups (§3) | 99.4 MB | 99.8 MB |
| `cam1.mp4` | scene camera colour video, 1280×720 | 266.4 MB | 271.8 MB |
| `cam2.mp4` | wrist camera colour video, 1280×720 | 293.0 MB | 285.0 MB |

Total **658,839,801 bytes** (`takes/`) + **656,642,118 bytes**
(`retimed_30hz/`) = **1.32 GB**, plus `assets/`, the
two `dataset_stats.json`, `retimed_30hz/retime_manifest.json` and this file.

Take names run `take_01_20260915_000901` … `take_23_20260915_001937`. **`take_06` is absent by
design** (discarded during recording) — 22 folders, no partial take anywhere. The two sets carry
the **same 22 take names**.

---

## 3. `vectors.h5` — top-level layout

One file per take. Root has **15 groups**: the **9 real-recorder groups** (schema identical to the
real release apart from the extra `stamp_s` column and a *filled* `synchronized`), plus **5 `sim_*`
groups** and `sim_scene`. Every group is a time series at its own native rate on its own clock,
with its own `t_rel_s` = seconds since take start. All datasets are **1-D `float64`**, one array
per channel (columnar: channel `foo` is dataset `group/foo`, NOT a 2-D table). The one exception
is `sim_scene/xml`, a scalar string dataset.

| Group | Rows (22 takes) | Native rate | What it is |
|---|--:|---|---|
| `cam1_frames` | 9,173 | **25.5 Hz** live / **30.0 Hz** retimed | Timestamp + frame index for each `cam1.mp4` frame |
| `cam2_frames` | 9,173 | **25.5 Hz** live / **30.0 Hz** retimed | Timestamp + frame index for each `cam2.mp4` frame |
| `command` | 44,660 | **125.5 Hz** | Commanded UR joint targets (the **action**), radians |
| `ur_joint_states` | 44,660 | **125.5 Hz** | Simulated joint state: position, velocity, effort |
| `tcp_pose` | 44,660 | **125.5 Hz** | TCP pose in base frame (position + quaternion) |
| `wrench` | 44,660 | **125.5 Hz** | Flange 6-axis force/torque sensor, tared at take start |
| `gripper` | 22,336 | **62.8 Hz** | Gripper command, measured position, leader trigger |
| `gello_joint_states` | 10,797 | **30.2 Hz** | GELLO **leader** joint pos + finite-difference vel |
| `synchronized` | 33,848 | 100 Hz | 56-channel fused table — ⚠️ **FILLED here**, empty in the real release (§3.5) |
| `sim_object_poses` | 10,650 | 30 Hz | **Ground-truth** carrot + pot pose (§3.7) |
| `sim_control` | 44,660 | 125 Hz | Teleop state machine, IK conditioning, per-tick task success (§3.7) |
| `sim_leader_filtered` | 44,660 | 125 Hz | One-Euro filter output the controller consumed (§3.7) |
| `sim_mj_state` | 44,660 | 125 Hz | **Full generalized state** `qpos`/`qvel`/`ctrl` (§3.7) |
| `sim_frame_capture` | 18,346 | ~51 Hz | Which physics tick each rendered frame came from (§3.7) |
| `sim_scene` | 22 | once / take | The **exact compiled MJCF** + asset manifest + layout + config (§3.8) |

### 3.1 Measured rates

| Group | mean rate (Hz) | min–max across takes | median Δt (ms) | 5th pct Δt (ms) | max gap (ms) | 1 / median Δt (Hz) |
|---|--:|--:|--:|--:|--:|--:|
| `cam1_frames` | 25.53 | 23.71 – 27.31 | 37.3 | 25.4 | 117.4 | 26.8 |
| `cam2_frames` | 25.47 | 23.42 – 27.27 | 37.3 | 25.5 | 112.9 | 26.8 |
| `command` | 125.50 | 120.30 – 125.86 | 7.8 | 0.8 | 79.3 | 128.2 |
| `ur_joint_states` | 125.50 | 120.30 – 125.86 | 7.8 | 0.8 | 79.4 | 128.2 |
| `tcp_pose` | 125.50 | 120.30 – 125.86 | 7.8 | 0.8 | 79.3 | 128.2 |
| `wrench` | 125.50 | 120.30 – 125.86 | 7.8 | 0.8 | 79.3 | 128.2 |
| `gripper` | 62.79 | 60.19 – 63.02 | 15.9 | 5.2 | 96.1 | 62.9 |
| `gello_joint_states` | 30.18 | 30.13 – 30.29 | 32.9 | 22.9 | 104.9 | 30.4 |
| `cam1_frames` **(retimed)** | 30.00 | 30.00 – 30.00 | 33.3 | 33.3 | 33.4 | 30.0 |
| `cam2_frames` **(retimed)** | 30.00 | 30.00 – 30.00 | 33.3 | 33.3 | 33.4 | 30.0 |

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

> **The robot streams run at 125 Hz, not the real rig's ~67 Hz.** The sim writes
> `ur_joint_states` / `command` / `tcp_pose` / `wrench` on every 2nd 2 ms physics step, so there
> are ~4.2 robot samples per retimed camera frame against ~2.2 on the real rig. The gripper table
> is written at half that (62.8 Hz) and the leader table at its 30 Hz USB sampling rate.

> **The live camera rate is the one real defect in this release.** `cam1_frames` / `cam2_frames`
> in `takes/` average **25.53 / 25.47 Hz** (range 23.4 – 27.3) while the MP4 is stamped 30 fps, so
> the live videos **play ~1.18× fast**. The timestamps are correct; only the container rate is
> wrong. `retimed_30hz/` fixes it by re-rendering: **30.00 Hz in every take, max Δt 33.4 ms.**
> See §9.

### 3.2 ⚠️ The `columns` attribute quirk (inherited from the real recorder)

Every group carries an HDF5 **attribute** named `columns` which is a **single scalar JSON string**,
not a native list — measured Python `str` in all 44 files
(`dataset_stats.json → aggregate.columns_attr_python_types == ["str"]`).
`list(grp.attrs["columns"])` iterates it character-by-character and yields garbage. **Always
`json.loads(...)`.** The correct column lists are given verbatim below and do not depend on the
attribute.

### 3.3 Per-group / per-channel schema — the nine real groups

All datasets `float64`, shape `(N,)`. Ranges are **measured pooled across all 22 takes** of
`takes/` (the retimed set differs only in `cam*_frames`).

#### `cam1_frames` / `cam2_frames` — camera frame timelines
| Channel | dtype | Unit | Meaning | Range |
|---|---|---|---|---|
| `t_rel_s` | float64 | s | Time of this frame, since take start | 0 → 27.82 |
| `frame_idx` | float64 | index | 0-based frame number in the MP4 (float-typed) | 0 → 698 live, 0 → 831 retimed |
| `stamp_s` | float64 | s (**unix epoch**) | Wall-clock time the frame was captured off the renderer | ~1.789e9 |

> ⚠️ In `retimed_30hz/` these two tables are **rewritten** onto the exact 30 Hz grid and are the
> only tables that change. Everything else is byte-for-byte the live recording.

#### `command` — commanded UR joint targets ➜ **the ACTION**
| Channel | dtype | Unit | Meaning | Range | Mean |
|---|---|---|---|---|---|
| `cmd1` | float64 | rad | Absolute target for joint 1 from EEF-delta IK | -4.0538 → -2.8535 | -3.3385 |
| `cmd2` | float64 | rad | Absolute target for joint 2 from EEF-delta IK | -2.0504 → -1.0596 | -1.5070 |
| `cmd3` | float64 | rad | Absolute target for joint 3 from EEF-delta IK | 1.4157 → 2.6139 | 1.9385 |
| `cmd4` | float64 | rad | Absolute target for joint 4 from EEF-delta IK | -2.9262 → -1.3392 | -1.9637 |
| `cmd5` | float64 | rad | Absolute target for joint 5 from EEF-delta IK | -2.0771 → -1.3681 | -1.6389 |
| `cmd6` | float64 | rad | Absolute target for joint 6 from EEF-delta IK | -4.0435 → -2.6561 | -3.1377 |
| `t_rel_s` | float64 | s | Time of this sample | 0 → 27.82 | — |

#### `ur_joint_states` — simulated joint state ➜ core of **observation.state**
| Channel | dtype | Unit | Meaning | Range | Mean |
|---|---|---|---|---|---|
| `q1` | float64 | rad | Measured joint 1 position (`d.qpos`) | -4.0390 → -2.8625 | -3.3338 |
| `q2` | float64 | rad | Measured joint 2 position (`d.qpos`) | -2.0367 → -1.0833 | -1.5041 |
| `q3` | float64 | rad | Measured joint 3 position (`d.qpos`) | 1.4209 → 2.6115 | 1.9395 |
| `q4` | float64 | rad | Measured joint 4 position (`d.qpos`) | -2.8825 → -1.3502 | -1.9604 |
| `q5` | float64 | rad | Measured joint 5 position (`d.qpos`) | -2.0592 → -1.3709 | -1.6391 |
| `q6` | float64 | rad | Measured joint 6 position (`d.qpos`) | -4.0384 → -2.6656 | -3.1348 |
| `qd1..qd6` | float64 | rad/s | Joint velocities (`d.qvel[:6]`) | -0.5612 → 0.5569 (pooled) | ≈0 |
| `eff1..eff6` | float64 | N·m | Actuator force (`d.actuator_force[:6]`) | -64.0041 → 107.1025 (pooled) | — |
| `t_rel_s` | float64 | s | Time of this sample | 0 → 27.82 | — |
| `stamp_s` | float64 | s (**unix epoch**) | Wall-clock time the physics thread published the tick | ~1.789e9 | — |

#### `tcp_pose` — tool-center-point pose (robot base frame)
| Channel | dtype | Unit | Meaning | Range | Mean |
|---|---|---|---|---|---|
| `x` | float64 | m | TCP x — `fk(q) ⊕ 0.174 m` along flange +Z | 0.2940 → 0.5914 | 0.4445 |
| `y` | float64 | m | TCP y — `fk(q) ⊕ 0.174 m` along flange +Z | -0.3670 → 0.2682 | 0.0268 |
| `z` | float64 | m | TCP z — `fk(q) ⊕ 0.174 m` along flange +Z | -0.0341 → 0.3300 | 0.1476 |
| `qw` | float64 | — | quaternion w — `fk(q) ⊕ 0.174 m` along flange +Z | -0.1283 → 0.1700 | 0.0029 |
| `qx` | float64 | — | quaternion x — `fk(q) ⊕ 0.174 m` along flange +Z | 0.4296 → 0.9395 | 0.7617 |
| `qy` | float64 | — | quaternion y — `fk(q) ⊕ 0.174 m` along flange +Z | 0.3219 → 0.9013 | 0.6241 |
| `qz` | float64 | — | quaternion z — `fk(q) ⊕ 0.174 m` along flange +Z | -0.3042 → 0.0954 | -0.0444 |
| `t_rel_s`, `stamp_s` | float64 | s | as above | — | — |

#### `wrench` — 6-axis force/torque at the flange
| Channel | dtype | Unit | Meaning | Range | Mean |
|---|---|---|---|---|---|
| `fx` | float64 | N | Simulated `<force>`/`<torque>` sensor at `ft_site`, **tared at take start** | -85.8618 → 91.0505 | 0.0651 |
| `fy` | float64 | N | Simulated `<force>`/`<torque>` sensor at `ft_site`, **tared at take start** | -10.6378 → 171.2164 | 1.1457 |
| `fz` | float64 | N | Simulated `<force>`/`<torque>` sensor at `ft_site`, **tared at take start** | -9.0993 → 279.1303 | 0.9739 |
| `tx` | float64 | N·m | Simulated `<force>`/`<torque>` sensor at `ft_site`, **tared at take start** | -25.8313 → 2.2475 | -0.1045 |
| `ty` | float64 | N·m | Simulated `<force>`/`<torque>` sensor at `ft_site`, **tared at take start** | -16.0800 → 23.2892 | 0.0140 |
| `tz` | float64 | N·m | Simulated `<force>`/`<torque>` sensor at `ft_site`, **tared at take start** | -4.1689 → 4.9055 | 0.0070 |
| `t_rel_s`, `stamp_s` | float64 | s | as above | — | — |

> ⚠️ **Unvalidated against the real arm.** The frame and sign convention of this sensor has never
> been measured against the real UR7e's wrench. Magnitudes are much larger than the real rig's
> because a 2 ms-timestep contact transient is stiff. Treat as a sim signal.

#### `gripper` — gripper signals (**0.0 = open, 1.0 = closed**, all three)
| Channel | dtype | Unit | Meaning | Range | Mean |
|---|---|---|---|---|---|
| `grip_pos` | float64 | normalized | Measured normalized opening of the 2F-85 driver joint | 0.0000 → 0.9150 | 0.3367 |
| `grip_cmd` | float64 | normalized | Commanded normalized opening ➜ the gripper **action** | 0.0000 → 0.9998 | 0.4046 |
| `gello_grip` | float64 | normalized | Raw GELLO trigger value | 0.0000 → 1.0000 | 0.4048 |
| `t_rel_s` | float64 | s | Time of this sample | 0 → 27.82 | — |

#### `gello_joint_states` — GELLO **leader** joints (teleop only, NOT for inference)
| Channel | dtype | Unit | Meaning | Range |
|---|---|---|---|---|
| `q1` | float64 | rad | Leader joint 1, unwrapped | -1.0324 → 0.2777 |
| `q2` | float64 | rad | Leader joint 2, unwrapped | -1.9269 → -0.8101 |
| `q3` | float64 | rad | Leader joint 3, unwrapped | 1.0642 → 2.1917 |
| `q4` | float64 | rad | Leader joint 4, unwrapped | -3.0860 → -1.6394 |
| `q5` | float64 | rad | Leader joint 5, unwrapped | -1.9248 → -0.8586 |
| `q6` | float64 | rad | Leader joint 6, unwrapped | -1.7614 → 0.9108 |
| `qd1..qd6` | float64 | rad/s | Finite-difference leader velocity | -2.2226 → 1.9564 |
| `t_rel_s` | float64 | s | Time of this sample | 0 → 27.82 |
| `stamp_s` | float64 | s (**MONOTONIC, not unix**) | Leader thread's own clock — ⚠️ a different timebase from every other `stamp_s` | 1715.1 → 2362.5 |

### 3.4 The leader stream does **not** mirror the follower

Same mechanism as the real EEF-mode release: `command` comes from **IK on a leader pose delta**,
so `gello_q*` is one IK branch of a *virtual* leader chain and `ur_q*` is another solution of a
different chain. They are related only through the end-effector pose delta, never joint-by-joint.
`gello_*` is leader-frame telemetry with its own zeros and sign conventions; it **cannot** be used
to reconstruct the action or the follower state, and a deployed policy cannot see it anyway. Use
`command` for the action and `ur_joint_states` for the state. Per-joint correlations are in
`dataset_stats.json → leader_vs_follower`.

### 3.5 `synchronized` — **FILLED here** (the real release's is empty)

A 56-channel fused table on a **100 Hz grid**, 33,848 rows over the 22 takes, starting only once
both cameras have delivered their first frame. Channels: `t_rel_s`, `t_wall`, `cmd1..6`,
`ur_q1..6`, `ur_qd1..6`, `ur_eff1..6`, `tcp_{x,y,z,qw,qx,qy,qz}`, `fx,fy,fz,tx,ty,tz`,
`gello_q1..6`, `gello_qd1..6`, `gello_grip`, `grip_cmd`, `grip_pos`, `cam1_frame_idx`,
`cam2_frame_idx`.

> 🪤 **In `retimed_30hz/`, `cam1_frame_idx` / `cam2_frame_idx` still point at the ORIGINAL live
> frame numbers.** Only `cam*_frames` was rewritten. Use `synchronized` for camera indexing with
> `takes/` only; with the retimed videos, index through `cam*_frames`.

### 3.6 ⚠️ Timestamps — nothing is stamped late, **do not shift anything**

The real release carries a recorder artefact: three groups stamped 0.900 s / ≈0.41 s late by
rclpy spin-thread starvation. **That mechanism does not exist here.** The sim recorder stamps
every robot row with the physics tick that produced it, and `command`, `ur_joint_states`,
`tcp_pose` and `wrench` all come out of the **same** tick — their `t_rel_s` agree to within
**0.3 ms**. Speed cross-correlation between them measures **0.000 s in all 22 takes**.

`dataset_stats.json → timestamp_lag` nevertheless reports a `ur_joint_states_lag_s` of
**0.190 – 0.205 s** (median **0.198 s**), with the joint residual falling from 0.0187 rad
uncorrected to 0.0037 rad at that shift. **That is not a clock error — it is the simulated arm's
mechanical tracking lag behind its commanded joint target**, bounded by the 250 Hz controller's
`max_step_rad = 0.0025` slew limit and the actuator gains. It is real behaviour of the simulated
plant, not an artefact to remove. The JSON says so itself:
`timestamp_lag.applies_to_this_release = false` and `timestamp_lag.simulation_note`; the `note` /
`method` prose in that block is inherited verbatim from the real release's script and describes
the *real* rig.

### 3.7 Simulation-only groups (`sim_*`)

#### `sim_object_poses` — **ground truth**, 30 Hz
| Channel | dtype | Unit | Meaning |
|---|---|---|---|
| `carrot_x`, `carrot_y`, `carrot_z` | float64 | m | Carrot body origin in world frame |
| `carrot_qx`, `carrot_qy`, `carrot_qz`, `carrot_qw` | float64 | — | Carrot orientation |
| `pot_x` … `pot_qw` | float64 | m / — | Same for the pot |
| `t_rel_s` | float64 | s | Time of this sample |

**Row 0 of this table is the authoritative initial layout of the take** — `sim_scene`'s `layout`
attribute is stale (§9). Measured across the 22 takes: carrot x **0.396 – 0.492 m**, y
**+0.124 – +0.242 m**; pot x **0.405 – 0.489 m**, y **−0.191 – −0.258 m**; both yaws vary freely.

#### `sim_control` — teleop state machine, 125 Hz
| Channel | dtype | Meaning |
|---|---|---|
| `engaged` | float64 | 1.0 while the operator holds the deadman and the controller drives the arm |
| `eef_state_code` | float64 | `0 DISENGAGED · 1 ENGAGING · 2 ENGAGED · 3 HOLD · 4 SOFT_START · 5 REJECTED · 6 FAULT · 7 PAUSED` (also in `sim_meta.eef_state_codes`) |
| `pos_scale` | float64 | Effective leader→follower position scale (1.0 throughout) |
| `sigma_min` | float64 | Smallest singular value of the Jacobian — IK conditioning |
| `gamma`, `ls_scale` | float64 | Damping / line-search scale inside the IK step |
| `task_success` | float64 | **1.0 on ticks where the geometric success test passes** |
| `sim_t`, `tick` | float64 | Simulator time (s) and physics tick index |
| `t_rel_s` | float64 | Time of this sample |

Measured over the release: `engaged` is 1.0 for **71.8 – 100 %** of ticks per take (median 100 %);
the state histogram is **43,022 ENGAGED · 1,482 HOLD · 156 DISENGAGED** ticks, with **no**
`REJECTED` or `FAULT` tick anywhere. Every take reaches `task_success` — first at **8.68 s**,
median **12.26 s**, last at **25.28 s**.

The success test (`sim_collect/task.py`): the food body's origin is inside the container's opening
cylinder (above `pot_floor`, below `pot_opening`, within `inner_radius` 0.09 m) **and** its
vertical speed is < 0.05 m/s **and** `grip_cmd < 0.3`. It is a **geometric approximation for the
operator's on-screen badge**, not a curated training label.

#### `sim_leader_filtered` — 125 Hz
`qf1..qf6` (rad) — the One-Euro filter output the `EefDeltaController` actually consumed, plus
`t_rel_s`. With `gello_joint_states` this lets you replay the controller offline and reproduce
`command`.

#### `sim_mj_state` — **the full generalized state**, 125 Hz
`qpos0..qpos27`, `qvel0..qvel25`, `ctrl0..ctrl6`, `sim_t`, `tick`, `t_rel_s`. Group attrs:
**`nq = 28`, `nv = 26`, `nu = 7`**, plus a `ctrl_note`. This is what makes the release
replayable — see §5.

#### `sim_frame_capture` — render provenance, ~51 Hz (both cameras interleaved)
`cam` (1 or 2), `frame_idx`, `seq`, `sim_t`, `tick`, `t_capture_rel_s`, `t_rel_s`. Tells you which
physics tick each **live** rendered frame came from. Unchanged in `retimed_30hz/`, where it
therefore still describes the *live* frames.

### 3.8 `/sim_scene` — the exact model

| Item | Type | Content |
|---|---|---|
| `xml` | scalar string dataset (~50 kB) | **The exact MJCF the simulator compiled** for this take |
| `xml_sha256` | attr | sha256 of that XML |
| `assets_manifest` | attr (JSON) | `{asset_filename: {sha256, bytes}}` for all **29** referenced meshes/textures. The bytes are **not** copied into the take — they live in the `sim_collect` repo at `git_commit` |
| `layout` | attr (JSON) | ⚠️ **stale — see §9.** Per-object `pos`/`yaw`/`fallback` plus `_seed`, `_attempt` |
| `layout_seed` | attr | ⚠️ `-1` in all 22 takes (§9) |
| `config` | attr (JSON) | The complete scene config: robot, physics, floor, cameras, render, objects, layout, task, control, leader, gripper |
| `config_path` | attr | `sim_collect/configs/carrot_in_pot_sim.yaml` |
| `git_commit`, `mujoco_version`, `timestep` | attrs | `4bac8657…`, `3.10.0`, `0.002` |

### 3.9 File attribute `sim_meta`

One JSON string on the root of `vectors.h5` (the real recorder writes no file attrs at all; adding
them breaks no consumer). Keys: `sim_collect_version`, `git_commit`, `mujoco_version`, `robot`,
`control_mode`, `gripper_mode`, `pos_scale`, `capture_fps`, `scene_meta`, `scene_sha`, `config`,
`scene_config`, `objects`, `chosen_food`, `container`, `layout_seed` (⚠️ §9), `cameras` (pose at
start + colour/depth render intrinsics per camera), `eef_state_codes`, `record_depth` (`false`),
`depth_camera_info` / `depth_extrinsics_depth_to_color` / `depth_source` (the real D435 sidecar
values, kept so sim and real takes share one schema even when depth is off), `simulated` (`true`),
`take_name`, `take_index`, `started_at`, `stopped_at`, `duration_s`, `camera_fps`,
`sample_rate_hz`, `recorder_counts`, `message_counts`, `achieved_fps`, `achieved_fps_take`,
`capture`, `problems`, `events`, `object_names`, `task_success_at_stop`. In `retimed_30hz/` there
is one extra key, **`retimed`**:
`{from, fps, frames, grid_start_t_rel_s, max_state_lookup_dt_s, note, original_problems}`.

---

## 4. Complete pooled value ranges

Every value channel of `takes/`, pooled over all 22 takes. The `stamp_s` and `frame_idx` clocks
are omitted (they are indices, not values); all of them are in
`dataset_stats.json → value_ranges`.

| Channel | min | max | mean | std |
|---|--:|--:|--:|--:|
| `command/cmd1` | -4.0538 | -2.8535 | -3.3385 | 0.3085 |
| `command/cmd2` | -2.0504 | -1.0596 | -1.5070 | 0.1769 |
| `command/cmd3` | 1.4157 | 2.6139 | 1.9385 | 0.1903 |
| `command/cmd4` | -2.9262 | -1.3392 | -1.9637 | 0.3142 |
| `command/cmd5` | -2.0771 | -1.3681 | -1.6389 | 0.1111 |
| `command/cmd6` | -4.0435 | -2.6561 | -3.1377 | 0.2375 |
| `gello_joint_states/q1` | -1.0324 | 0.2777 | -0.3984 | 0.3341 |
| `gello_joint_states/q2` | -1.9269 | -0.8101 | -1.3865 | 0.1902 |
| `gello_joint_states/q3` | 1.0642 | 2.1917 | 1.7367 | 0.2008 |
| `gello_joint_states/q4` | -3.0860 | -1.6394 | -2.2805 | 0.3149 |
| `gello_joint_states/q5` | -1.9248 | -0.8586 | -1.4892 | 0.1773 |
| `gello_joint_states/q6` | -1.7614 | 0.9108 | -0.3210 | 0.5730 |
| `gello_joint_states/qd1` | -1.1027 | 1.0749 | 0.0203 | 0.1746 |
| `gello_joint_states/qd2` | -1.1159 | 0.8295 | 0.0035 | 0.1655 |
| `gello_joint_states/qd3` | -0.8712 | 0.9026 | -0.0033 | 0.1490 |
| `gello_joint_states/qd4` | -0.8537 | 1.4665 | 0.0043 | 0.2265 |
| `gello_joint_states/qd5` | -1.1402 | 1.2635 | -0.0134 | 0.1294 |
| `gello_joint_states/qd6` | -2.2226 | 1.9564 | 0.0227 | 0.3238 |
| `gripper/gello_grip` | 0.0000 | 1.0000 | 0.4048 | 0.4852 |
| `gripper/grip_cmd` | 0.0000 | 0.9998 | 0.4046 | 0.4839 |
| `gripper/grip_pos` | 0.0000 | 0.9150 | 0.3367 | 0.4031 |
| `tcp_pose/qw` | -0.1283 | 0.1700 | 0.0029 | 0.0608 |
| `tcp_pose/qx` | 0.4296 | 0.9395 | 0.7617 | 0.0925 |
| `tcp_pose/qy` | 0.3219 | 0.9013 | 0.6241 | 0.1137 |
| `tcp_pose/qz` | -0.3042 | 0.0954 | -0.0444 | 0.0558 |
| `tcp_pose/x` | 0.2940 | 0.5914 | 0.4445 | 0.0466 |
| `tcp_pose/y` | -0.3670 | 0.2682 | 0.0268 | 0.1592 |
| `tcp_pose/z` | -0.0341 | 0.3300 | 0.1476 | 0.0970 |
| `ur_joint_states/eff1` | -64.0041 | 30.0950 | 0.0293 | 2.1257 |
| `ur_joint_states/eff2` | -11.0136 | 107.1025 | -4.4148 | 4.7394 |
| `ur_joint_states/eff3` | -13.2352 | 31.3021 | -4.5149 | 1.5355 |
| `ur_joint_states/eff4` | -18.0977 | 10.5806 | -0.9808 | 0.7682 |
| `ur_joint_states/eff5` | -27.0023 | 28.0000 | -0.0896 | 1.1235 |
| `ur_joint_states/eff6` | -4.6147 | 4.9129 | 0.0185 | 0.2001 |
| `ur_joint_states/q1` | -4.0390 | -2.8625 | -3.3338 | 0.3055 |
| `ur_joint_states/q2` | -2.0367 | -1.0833 | -1.5041 | 0.1743 |
| `ur_joint_states/q3` | 1.4209 | 2.6115 | 1.9395 | 0.1899 |
| `ur_joint_states/q4` | -2.8825 | -1.3502 | -1.9604 | 0.3121 |
| `ur_joint_states/q5` | -2.0592 | -1.3709 | -1.6391 | 0.1087 |
| `ur_joint_states/q6` | -4.0384 | -2.6656 | -3.1348 | 0.2339 |
| `ur_joint_states/qd1` | -0.4522 | 0.3217 | -0.0232 | 0.1258 |
| `ur_joint_states/qd2` | -0.4105 | 0.4322 | -0.0035 | 0.1260 |
| `ur_joint_states/qd3` | -0.4947 | 0.4158 | 0.0063 | 0.1191 |
| `ur_joint_states/qd4` | -0.5591 | 0.5569 | -0.0065 | 0.2213 |
| `ur_joint_states/qd5` | -0.3949 | 0.4152 | 0.0017 | 0.0895 |
| `ur_joint_states/qd6` | -0.5612 | 0.5366 | -0.0146 | 0.1229 |
| `wrench/fx` | -85.8618 | 91.0505 | 0.0651 | 4.4609 |
| `wrench/fy` | -10.6378 | 171.2164 | 1.1457 | 6.7999 |
| `wrench/fz` | -9.0993 | 279.1303 | 0.9739 | 11.6699 |
| `wrench/tx` | -25.8313 | 2.2475 | -0.1045 | 0.9711 |
| `wrench/ty` | -16.0800 | 23.2892 | 0.0140 | 0.8081 |
| `wrench/tz` | -4.1689 | 4.9055 | 0.0070 | 0.1627 |

---

## 5. Reconstruction — rebuilding and replaying a take

`/sim_scene` + `sim_mj_state` reproduce every recorded instant **kinematically exactly**. The
replay tool `sim_collect/tools/replay_take.py`:

1. reads `config` + `layout` and rebuilds the scene to obtain the asset files;
2. checks every asset against its recorded sha256 in `assets_manifest`;
3. compiles the **stored** `xml` (not a regenerated one) against those assets — reporting
   `rebuilt xml matches: True`;
4. for each `sim_mj_state` row writes `qpos`/`qvel` and calls `mj_forward` — **no physics is
   re-simulated**, so there is no divergence;
5. optionally renders any camera at any stride.

```bash
# in a checkout of the recording repo at sim_meta.git_commit (4bac865)
.venv/bin/python -m sim_collect.tools.replay_take <take_dir> --check
#  -> model: nq 28 nv 26 nu 7 | rows 3002 | rebuilt xml matches: True | assets 29
#     object pose reconstruction error (replayed vs recorded sim_object_poses): max 0.00 mm

MUJOCO_GL=glfw DISPLAY=:0 .venv/bin/python -m sim_collect.tools.replay_take <take_dir> --viewer
MUJOCO_GL=glfw DISPLAY=:0 .venv/bin/python -m sim_collect.tools.replay_take <take_dir> \
    --render cam1 cam2 --out /tmp/frames --every 15
```

Measured object-pose reconstruction error: **0.00 mm**, including on takes whose stored `layout`
attribute is stale — because step 4 overwrites `qpos` regardless. `retimed_30hz/` was produced
exactly this way.

---

## 6. Camera timelines and the colour videos

`cam*.mp4` are 1280×720 `mpeg4` `yuv420p` stamped 30 fps in **both** sets. Map frames to signals
through `cam*_frames`:

| | `takes/` (live) | `retimed_30hz/` |
|---|--:|--:|
| cam1 frames | 9,173 | **10,725** |
| cam2 frames | 9,173 | **10,725** |
| mean frame rate | 25.53 Hz (23.71 – 27.31) | **30.00 Hz every take** |
| median Δt / max Δt | 37.3 ms / 117.4 ms | 33.3 ms / **33.4 ms** |
| takes with cam1 ≠ cam2 count | **20 of 22** (−9 … +5) | **0** |
| `sim_meta.problems` | 2 entries per take | `[]` |

`retime_manifest.json` records, per take, the output frame count and `max_state_lookup_dt_s` — the
largest distance between a 30 Hz grid instant and the nearest recorded 125 Hz state row. **Worst
over all 22 takes: 37.6 ms**; `take_01` is 19.6 ms.

**Video frame counts match `cam*_frames` row counts exactly in all 88 videos**, verified with
`ffprobe -count_frames`.

---

## 7. Per-take statistics

| Take | Duration (s) | live cam1/cam2 | retimed cam | live cam rate (Hz) | robot rate (Hz) | gripper closures | max `grip_pos` |
|---|--:|--:|--:|--:|--:|--:|--:|
| `take_01_20260915_000901` | 15.72 | 395 / 397 | 469 | 25.33 / 25.38 | 125.7 | 1 | 0.7897 |
| `take_02_20260915_000933` | 15.66 | 403 / 402 | 468 | 25.86 / 25.78 | 124.0 | 1 | 0.9132 |
| `take_03_20260915_001003` | 15.96 | 400 / 400 | 477 | 25.18 / 25.14 | 123.0 | 1 | 0.9119 |
| `take_04_20260915_001042` | 16.96 | 446 / 450 | 507 | 26.43 / 26.62 | 125.5 | 2 | 0.9140 |
| `take_05_20260915_001112` | 11.66 | 314 / 312 | 348 | 27.09 / 26.90 | 125.5 | 1 | 0.5652 |
| `take_07_20260915_001145` | 12.68 | 308 / 307 | 379 | 24.38 / 24.37 | 125.2 | 1 | 0.9141 |
| `take_08_20260915_001209` | 24.01 | 612 / 611 | 717 | 25.61 / 25.54 | 125.5 | 2 | 0.9121 |
| `take_09_20260915_001242` | 27.82 | 699 / 700 | 832 | 25.19 / 25.20 | 124.2 | 2 | 0.9131 |
| `take_10_20260915_001322` | 12.28 | 299 / 297 | 367 | 24.44 / 24.34 | 120.3 | 1 | 0.9140 |
| `take_11_20260915_001346` | 12.16 | 314 / 318 | 363 | 26.03 / 26.30 | 124.8 | 1 | 0.9150 |
| `take_12_20260915_001405` | 20.57 | 537 / 540 | 615 | 26.19 / 26.39 | 124.9 | 1 | 0.9144 |
| `take_13_20260915_001438` | 13.01 | 349 / 353 | 388 | 26.96 / 27.27 | 125.7 | 1 | 0.9113 |
| `take_14_20260915_001502` | 21.13 | 552 / 547 | 631 | 26.25 / 26.05 | 125.6 | 2 | 0.9117 |
| `take_15_20260915_001534` | 12.84 | 342 / 337 | 383 | 26.84 / 26.34 | 125.7 | 1 | 0.9119 |
| `take_16_20260915_001558` | 14.02 | 380 / 377 | 418 | 27.31 / 27.01 | 125.6 | 1 | 0.9138 |
| `take_17_20260915_001620` | 22.85 | 580 / 589 | 683 | 25.47 / 25.86 | 124.4 | 2 | 0.9135 |
| `take_18_20260915_001658` | 11.30 | 283 / 284 | 337 | 25.17 / 25.25 | 122.8 | 1 | 0.9106 |
| `take_19_20260915_001725` | 16.37 | 417 / 413 | 489 | 25.59 / 25.36 | 125.5 | 1 | 0.5671 |
| `take_20_20260915_001805` | 19.63 | 496 / 497 | 586 | 25.38 / 25.40 | 125.6 | 2 | 0.9130 |
| `take_21_20260915_001833` | 19.35 | 489 / 489 | 578 | 25.38 / 25.37 | 125.5 | 2 | 0.9132 |
| `take_22_20260915_001910` | 11.86 | 291 / 290 | 353 | 24.73 / 24.61 | 125.9 | 1 | 0.9123 |
| `take_23_20260915_001937` | 11.29 | 267 / 263 | 337 | 23.71 / 23.42 | 125.6 | 1 | 0.9127 |

Duration: median **15.69 s**, min **11.29 s** (`take_23_20260915_001937`),
max **27.82 s** (`take_09_20260915_001242`).

---

## 8. How to load (h5py)

```python
import h5py, json, numpy as np, cv2

take = "retimed_30hz/take_03_20260915_001003"   # train on the retimed set
with h5py.File(f"{take}/vectors.h5", "r") as f:
    ur_q   = np.stack([f["ur_joint_states"][f"q{k+1}"][:] for k in range(6)], 1)  # (N125,6) rad
    cmd    = np.stack([f["command"][f"cmd{k+1}"][:]       for k in range(6)], 1)  # (N125,6) rad
    grip   = f["gripper"]["grip_pos"][:]                                          # (N63,)
    cam1_t = f["cam1_frames"]["t_rel_s"][:]                                       # 30 Hz master grid
    ur_t   = f["ur_joint_states"]["t_rel_s"][:]      # NO shift - see 3.6

    # simulator-only ground truth
    carrot = np.stack([f["sim_object_poses"][f"carrot_{k}"][:] for k in "xyz"], 1)
    succ   = f["sim_control"]["task_success"][:]
    qpos   = np.stack([f["sim_mj_state"][f"qpos{i}"][:]
                       for i in range(f["sim_mj_state"].attrs["nq"])], 1)
    mjcf   = f["sim_scene"]["xml"][()]               # the exact model, as bytes
    meta   = json.loads(f.attrs["sim_meta"])

# streams are at DIFFERENT rates - align to the camera grid by nearest timestamp,
# never by index:
ur_on_cam = np.stack([np.interp(cam1_t, ur_t, ur_q[:, k]) for k in range(6)], 1)

cap = cv2.VideoCapture(f"{take}/cam1.mp4")        # 1280x720 colour @ 30 fps
```

---

## 9. Anomalies & data-quality notes

1. 🐛 **`layout_seed` / `layout` metadata is stale in all 22 takes.** `sim_meta.layout_seed` says
   `0`, `/sim_scene` attr `layout_seed` says `-1`, and `/sim_scene` attr `layout` carries one
   single layout (carrot `(0.480, +0.188)`, pot `(0.488, −0.243)`) in **every** take — while the
   scene really was re-randomized between takes. A recorder bug, fixed after this session.
   **Read `sim_object_poses` row 0 (or `sim_mj_state` `qpos`) for the true initial layout.**
   `take_02`/`take_03` share a layout (no reset between them), as do `take_22`/`take_23`.
   Reconstruction is unaffected (§5): replay overwrites `qpos`, so the error stays 0.00 mm.
2. ⚠️ **`synchronized/cam*_frame_idx` in `retimed_30hz/` refers to the ORIGINAL live frames**
   (§3.5).
3. ⚠️ **Nothing is stamped late** — do not apply the real release's −0.900 s / −0.41 s fix (§3.6).
4. ⚠️ **`gello_joint_states/stamp_s` is a monotonic clock**, every other `stamp_s` is unix epoch
   (§3.3). `t_rel_s` is the common timebase.
5. **Live camera rate 23.4 – 27.3 fps against a 30 fps container** — the live videos play ~1.18×
   fast. Cause: software-GL rendering under CPU load. The recorder flags it per take in
   `sim_meta.problems` and `sim_meta.achieved_fps_take`. `retimed_30hz/` is the fix; the largest
   grid-snap state-lookup error it introduces is **37.6 ms** (§6).
6. **`cam1` / `cam2` frame counts differ in 20 of 22 live takes** (−9 … +5), because the two
   cameras render on independent workers. Equal in every retimed take. **Map by nearest
   `t_rel_s`, never by index.**
7. **Seven takes contain more than one gripper closure** (`grip_cmd ≥ 0.7` rising edge):
   `take_04`, `take_08`, `take_09`, `take_14`, `take_17`, `take_20`, `take_21` — a missed first
   grasp followed by a successful re-grasp (29 closures over 22 takes). All seven finish the task
   and all are **included**.
8. **`grip_pos` while carrying the carrot ranges 0.565 – 0.913**, not the real rig's tight
   0.47 – 0.66 plateau: the sim carrot is tapered and the contact is soft, so how far the fingers
   close depends on where along the taper the grasp landed. Fully open is **0.000 – 0.003**. Use
   `sim_object_poses` if you need to know what is held.
9. **`wrench` is unvalidated** against the real arm (§3.3).
10. **`synchronized` is filled** here and empty in the real release — the only structural schema
    difference (§3.5).
11. **Zero NaN and zero Inf** in any channel of any group of any take, both sets.
12. **Cleanliness:** exactly three files per take folder, no stray files, no hidden files, no
    sub-directories, in all 44 folders. The only absolute path is `sim_meta.retimed.from` in each
    retimed `vectors.h5`.
13. **Duration outliers, not defects:** `take_09` (27.82 s) and `take_08` (24.01 s) run about
    1.6× the median take length (15.69 s) — slower demonstrations. Their data is clean.
14. **First simulated session on this stack.** These 22 takes are the first real GELLO→MuJoCo
    teleop takes ever recorded here; expect a less fluent motion style than the real-robot
    release's.

---

## 10. Reproducing these numbers

```bash
python3 scripts/dataset/make_carrot_raw_stats.py \
    --data <staging>/takes --out <staging>/dataset_stats.json \
    --dataset Bigenlight/carrot_in_pot_sim_raw
python3 scripts/dataset/make_carrot_raw_stats.py \
    --data <staging>/retimed_30hz --out <staging>/retimed_30hz/dataset_stats.json \
    --dataset Bigenlight/carrot_in_pot_sim_raw
```

Depth-free mode is **auto-detected** from the takes on disk (no `depth.h5` present); `--no-depth`
forces it and `--depth` demands the real release's four-file set. Requires `h5py`, `numpy`, `cv2`
and `ffprobe` on `PATH`.