# Data Dictionary — `Bigenlight/banana_in_pot_raw`

Raw teleoperation recordings for the task **"put the right banana in the pot"**, captured
via a **GELLO leader → UR7e follower** setup with two RGB cameras.

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
datasets (`banana_in_pot`, `banana_in_pot_ee`) and `convert_to_lerobot.py`.

- **Scale:** 51 episodes (`take_*`) · 21,524 camera frames · ~717.4 s (~12 min) · ~745 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 51 files; **no absolute-path leakage** and no PII inside the H5.

---

## 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** | Binary open/close command (`grip_cmd`) plus a continuous measured position (`grip_pos`), and the leader-side `gello_grip`. |
| **Camera 1** | **Intel RealSense D435** — RGB only. |
| **Camera 2** | **Intel RealSense D435if** (D435 variant) — RGB only. |
| **Video format** | 1280×720 (720p), **30 fps**, MPEG-4 (`mpeg4`), color `yuv420p`. **No depth / no IR** recorded despite RealSense capability — color stream only. LeRobot copies re-encode to AV1; RAW keeps MPEG-4. |
| **Viewpoints** | Two fixed physical viewpoints (one tripod/third-person, one workspace). `cam1`↔`cam2` order is meaningful — preserve it at deploy time. |
| **Scene** | Table with distractors (2 bananas, apple, carrots/peppers, watermelon slice), a silver pot, and an ArUco/AprilTag fiducial. Success = the *right* banana placed in the pot. |

---

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

One file per take. Root has **9 groups**, 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 (typical) | Native rate | What it is |
|---|---|---|---|
| `cam1_frames` | = cam1 frame count | 30 Hz | Timestamp + frame index for each Camera-1 video frame |
| `cam2_frames` | = cam2 frame count | 30 Hz | Timestamp + frame index for each Camera-2 video frame |
| `command` | ~2× cam | **~56–60 Hz** | Commanded UR7e joint targets (the **action**), radians |
| `ur_joint_states` | ~2× cam | **~56–60 Hz** | Measured UR7e joint state: position, velocity, effort |
| `tcp_pose` | ~2× cam | **~56–60 Hz** | Measured tool-center-point pose (position + quaternion) |
| `wrench` | ~2× cam | **~56–60 Hz** | 6-axis force/torque at the TCP |
| `gripper` | ~1.2× cam | **~36–37 Hz** | Gripper command, measured position, leader grip |
| `gello_joint_states` | = cam | **~30 Hz** | GELLO **leader** joint pos + vel (teleop only — not for inference) |
| `synchronized` | **0 (EMPTY)** | — | Intended fused/aligned table; **empty in all 51 takes** — ignore |

> Rate note: the spec quotes round figures (~56 Hz / ~37 Hz). Measured average rates across the
> 51 takes are: command/ur/tcp/wrench median **~59.6 Hz** (range 40–75), gripper median **~35.9 Hz**
> (35.5–38.7), gello/cameras **~30 Hz**. Rates vary per take because logging is timestamp-driven,
> not fixed-period — always resample using `t_rel_s`, never assume a fixed dt.

### ⚠️ 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", "cmd1", ...]'` — NOT a native list/array. h5py returns it as a Python `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', 'e', 'l', ...]` — 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.

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

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

### `command` — commanded UR7e joint targets  ➜ **the ACTION**
| Channel | Unit | Meaning |
|---|---|---|
| `t_rel_s` | s | Timestamp |
| `cmd1`…`cmd6` | rad | Absolute target angle for UR7e joints 1–6 (base→wrist3) |

### `ur_joint_states` — measured UR7e joint state  ➜ core of **observation.state**
| Channel | Unit | Meaning |
|---|---|---|
| `t_rel_s` | s | Timestamp |
| `q1`…`q6` | rad | Measured joint angle, joints 1–6 |
| `qd1`…`qd6` | rad·s⁻¹ | Measured joint velocity, joints 1–6 |
| `eff1`…`eff6` | N·m (motor effort/current-proxy) | Measured joint effort/torque, joints 1–6 |

### `tcp_pose` — measured tool-center-point pose (base frame)
| Channel | Unit | Meaning |
|---|---|---|
| `t_rel_s` | s | Timestamp |
| `x`, `y`, `z` | m | TCP position in robot base frame |
| `qx`, `qy`, `qz`, `qw` | unit quaternion | TCP orientation (x,y,z,w order) |

### `wrench` — 6-axis force/torque at TCP
| Channel | Unit | Meaning |
|---|---|---|
| `t_rel_s` | s | Timestamp |
| `fx`, `fy`, `fz` | N | Force along base/TCP x,y,z |
| `tx`, `ty`, `tz` | N·m | Torque about x,y,z |

### `gripper` — gripper signals
| Channel | Unit | Meaning |
|---|---|---|
| `t_rel_s` | s | Timestamp |
| `gello_grip` | normalized | Leader (GELLO) grip trigger — teleop only |
| `grip_cmd` | normalized/binary open→close | Commanded gripper (part of the **action**). ⚠ all-NaN in one take — see anomalies |
| `grip_pos` | normalized 0–1 | Measured gripper opening (part of **observation.state**) |

### `gello_joint_states` — GELLO **leader** joints (teleop only, NOT for inference)
| Channel | Unit | Meaning |
|---|---|---|
| `t_rel_s` | s | Timestamp |
| `q1`…`q6` | rad | Leader joint angle |
| `qd1`…`qd6` | rad·s⁻¹ | Leader joint velocity |

### `synchronized` — **EMPTY in all takes** (do not use)
Group exists with 56 declared channels (`t_rel_s, t_wall, gello_q1..6, gello_qd1..6, gello_grip,
cmd1..6, ur_q1..6, ur_qd1..6, ur_eff1..6, 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`) but **every dataset
has shape `(0,)`**. Fusion was done downstream at conversion time, not stored here. Ignore it.

---

## 4. How to load (h5py)

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

path = "Put_right_banana_in_the_pot/take_01_20260707_154624/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
    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)
    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 — align to the camera grid by nearest timestamp:
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]`.

## 5. How to convert to LeRobot

Use `convert_to_lerobot.py` (repo root). 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)
- `gello_*` streams are intentionally dropped (not observable at inference).
- The all-NaN `grip_cmd` take is repaired by forward/back fill (`ffill_bfill`).

```bash
lr_env/bin/python convert_to_lerobot.py --data Put_right_banana_in_the_pot \
    --out banana_in_pot_lerobot --repo-id Bigenlight/banana_in_pot
```
The extended-EE variant additionally exposes `observation.tcp_pose` (7) and `observation.wrench` (6).

---

## 6. Anomalies & data-quality notes

- **All-NaN `grip_cmd`:** `take_01_20260707_180606` has `gripper/grip_cmd` = **NaN for all 333 rows**.
  `grip_pos` and `gello_grip` are fine. Converters must fill (fwd/back) or drop this channel for that
  take. No other NaNs anywhere in the dataset.
- **Camera frame-count mismatch:** in **21 / 51** takes `cam1` and `cam2` differ by ±1–2 frames
  (totals: 21,524 cam1 vs 21,515 cam2). Handle by nearest-timestamp mapping between the two camera
  clocks (as the converter does) — never assume cam1[k] and cam2[k] are simultaneous.
- **`synchronized/` empty** in all 51 takes (see §3).
- **Take-number gaps `{9, 23, 30, 35}` are by design** (aborted/discarded takes); folder names span
  take_01…take_36 across four recording sessions (timestamps 15:46, 16:00, 17:37, 18:06 on
  2026-07-07), and re-takes bring the folder count to exactly **51**. Do not treat gaps as missing data.
- **`columns` attribute is a JSON string, not a list** — always `json.loads` it (see §2).
- Cleanliness: 0 stray files, 0 hidden files, no sub-directories, and no `/home/` (absolute path)
  strings inside any of the 51 H5 files.

See `dataset_stats.json` for exact per-take frame counts, durations, and rates.