Initial: 200 CheatCode episodes (100 SFP + 100 SC)

README.md

@@ -0,0 +1,276 @@
+# AIC CheatCode Dataset
+
+LeRobot-compatible dataset of expert-teacher cable-insertion trajectories,
+collected inside the AI for Industry Challenge (AIC) Gazebo simulation with
+`ground_truth:=true` so every port/plug/TCP pose is logged exactly. The
+teacher is the reference **CheatCode** policy (`aic_example_policies.ros.CheatCode`).
+
+## What is in it
+
+- **N episodes** — one per trial. Each episode is a full approach + insertion
+  attempt (truncated just after the closest approach, so the steady-state
+  post-insertion hold is mostly absent).
+- Two trial types, interleaved by episode index:
+  - `sfp`  — SFP module → SFP port on a NIC card
+  - `sc`   — SC plug → SC optical port
+- Three wrist-mounted cameras at 1152×1024 RGB, 20 Hz, H.264 CRF 23 (mp4).
+- Full per-frame ground-truth labels for the target port's 6D pose and 9
+  cuboid keypoints, plus the plug, the TCP and the task board.
+
+Successes and partial / failed trials are saved the same way; the distinction
+lives in `meta/episodes.jsonl` (`success_tier3`).
+
+## Layout
+
+```
+<dataset_root>/
+├── README.md                                          # this file
+├── meta/
+│   ├── info.json                                      # dataset schema + fps
+│   ├── episodes.jsonl                                 # one row per episode
+│   ├── tasks.jsonl                                    # task type dictionary
+│   └── validator_report.json                          # (optional) sanity audit
+├── data/chunk-000/
+│   └── episode_XXXXXX.parquet                         # 20 Hz time series
+├── videos/chunk-000/
+│   ├── observation.images.left/episode_XXXXXX.mp4
+│   ├── observation.images.center/episode_XXXXXX.mp4
+│   └── observation.images.right/episode_XXXXXX.mp4
+└── assets_snapshot/
+    ├── camera_info.json                               # K, D, size, cam←tcp static
+    ├── keypoints_sfp.json                             # port frame 9-keypoint spec
+    └── keypoints_sc.json
+```
+
+## Parquet schema (per frame)
+
+### Indexing
+
+| column | type | meaning |
+|---|---|---|
+| `timestamp` | float | seconds since the first frame of this episode (wall clock) |
+| `frame_index` | int | 0-based within the episode |
+| `episode_index` | int | 0-based, unique within the dataset |
+| `task_index` | int | 0 = SFP trial, 1 = SC trial |
+
+### Observations
+
+| column | shape | units | notes |
+|---|---|---|---|
+| `observation.images.{left, center, right}` | `(H, W, 3)` uint8 RGB | pixels | stored in mp4; read with imageio / cv2 / lerobot loader |
+| `observation.state.joints` | `(7,)` | rad (joint 1-6) + m (gripper width, index 7) | current joint positions |
+| `observation.state.gripper` | `(1,)` or None | m | Gazebo's `/gripper_state`; may be null when the stock eval container doesn't publish it |
+| `observation.wrench` | `(6,)` | N, N·m | `[fx, fy, fz, tx, ty, tz]` at the F/T sensor |
+| `observation.tcp_pose_actual` | `(7,)` | m + unit quat (xyzw) | current gripper TCP in base_link: `[x, y, z, qx, qy, qz, qw]` |
+| `observation.tcp_pose_target` | `(7,)` | m + quat | controller reference TCP |
+| `observation.tcp_velocity` | `(6,)` | m/s + rad/s | `[vx, vy, vz, ωx, ωy, ωz]` |
+
+### Actions (teacher commands)
+
+| column | shape | notes |
+|---|---|---|
+| `action.pose_command` | `(7,)` or None | last `MotionUpdate.pose` published by CheatCode (xyz + xyzw quat in base_link) |
+| `action.joint_command` | `(7,)` or None | Joint-mode command; always `None` with CheatCode (Cartesian-only) |
+
+### Labels (ground truth, always populated)
+
+All 6D poses are `[x, y, z, qx, qy, qz, qw]` in the robot's **base_link** frame.
+
+| column | shape | meaning |
+|---|---|---|
+| `labels.port_pose_base` | `(7,)` | target port's link pose |
+| `labels.plug_pose_base` | `(7,)` | plug tip link pose |
+| `labels.gripper_tcp_pose_base` | `(7,)` | `gripper/tcp` |
+| `labels.task_board_pose_base` | `(7,)` | task board base link |
+| `labels.port_keypoints_3d_base` | `(9, 3)` | 9 keypoints defining the port cavity as a 3D cuboid, transformed into base_link |
+| `labels.port_keypoints_2d` | `dict[cam→(9, 2)]` | per-camera pixel projection of the 9 keypoints (already distortion-rectified) |
+| `labels.port_keypoints_visible` | `dict[cam→(9,)]` uint8 | 1 = keypoint is in front of the camera AND inside the image bounds |
+| `labels.port_bbox_2d` | `dict[cam→(4,)]` or None | axis-aligned 2D bbox of the visible keypoints: `[x0, y0, x1, y1]` |
+| `labels.all_ports` | `list[dict]` | Per-port labels for every port physically present on the active module. See below. |
+
+The `labels.port_*` columns above only cover the **target** port for the trial.
+`labels.all_ports` contains labels for **every** port visible in the scene so
+perception models can learn to detect all ports + select the target using the
+challenge's `Task.port_name` (which says "insert into `sfp_port_0` or
+`sfp_port_1`" for SFP trials). SFP trials: 2 entries
+(`sfp_port_0`, `sfp_port_1`); SC trials: 1 entry (`sc_port_base`).
+
+Each element is a dict:
+| key | shape | notes |
+|---|---|---|
+| `port_name` | str | `sfp_port_0`, `sfp_port_1`, or `sc_port_base` |
+| `is_target` | bool | True for the entry matching `Task.port_name` |
+| `pose_base` | `(7,)` | 6D pose in `base_link` |
+| `keypoints_3d_base` | `(9, 3)` | 9 keypoints in `base_link` |
+| `keypoints_2d` | `dict[cam→(9, 2)]` | per-camera pixel projection |
+| `keypoints_visible` | `dict[cam→(9,)]` uint8 | per-camera visibility |
+| `bbox_2d` | `dict[cam→(4,)]` or None | per-camera axis-aligned bbox |
+
+Keypoint order is identical to the `names` list in `assets_snapshot/keypoints_{sfp,sc}.json`:
+`[center, mouth_tl, mouth_tr, mouth_bl, mouth_br, base_tl, base_tr, base_bl, base_br]`.
+The 4 `mouth_*` points lie on the port entrance plane; the 4 `base_*` points
+are the same rectangle translated into the port along the local +Z axis.
+
+## episodes.jsonl (per episode)
+
+```json
+{
+  "episode_index": 0,
+  "task_index": 0,
+  "length": 312,
+  "trial_type": "sfp",
+  "trial_id": "trial_1",
+  "seed": 100000,
+  "randomization": {
+    "board_pose": {"x": ..., "y": ..., "yaw": ...},
+    "rails": { "nic_rail_0": {"entity_present": true, "entity_pose": {...}}, ... },
+    "grasp": { "gripper_offset": {...}, "roll": ..., "pitch": ..., "yaw": ... },
+    "cable_type": "sfp_sc_cable"
+  },
+  "target": {
+    "cable_name": "cable_0", "plug_type": "sfp", "plug_name": "sfp_tip",
+    "port_type": "sfp", "port_name": "sfp_port_0",
+    "target_module_name": "nic_card_mount_3", "time_limit": 180
+  },
+  "scoring": { /* full aic_engine scoring.yaml subsection */ },
+  "success_tier3": 75.0
+}
+```
+
+- `success_tier3` — scalar: 75 = full insertion, 38–50 = partial insertion,
+  0–25 = proximity only, negative = wrong port (rare).
+- `randomization` — full sampled scene description (reproduces the scene
+  given the seed).
+
+## Coordinate frames
+
+- `base_link` is the UR5e base; all `*_pose_base` labels are expressed in it.
+- `gripper/tcp` is the tool frame at the fingertip midpoint.
+- Camera `{left, center, right}` optical frames are rigidly mounted on the
+  wrist. Their extrinsic wrt `gripper/tcp` is constant and stored in
+  `assets_snapshot/camera_info.json` as `cam_T_tcp` (4×4, camera-from-tcp).
+  Per-frame camera-from-base is `cam_T_tcp @ inv(tcp_pose_actual_matrix)`.
+- Port frame origin is **inside the cavity**, not at the entrance. The
+  entrance plane is at local `z = z_mouth` (see
+  `assets_snapshot/keypoints_{sfp,sc}.json`), and CheatCode uses `z_offset`
+  relative to this convention.
+
+## Minimal loading recipe
+
+```python
+import json, pathlib, pandas as pd, imageio, numpy as np
+
+DS = pathlib.Path("<dataset_root>")
+
+# --- episodes metadata ---
+eps = [json.loads(l) for l in open(DS/"meta"/"episodes.jsonl")]
+print(len(eps), "episodes")
+
+# --- one episode: parquet + mp4 aligned by frame_index ---
+ep = eps[0]
+chunk = ep["episode_index"] // 1000
+df = pd.read_parquet(DS/f"data/chunk-{chunk:03d}/episode_{ep['episode_index']:06d}.parquet")
+video = imageio.get_reader(
+    str(DS/f"videos/chunk-{chunk:03d}/observation.images.center/"
+        f"episode_{ep['episode_index']:06d}.mp4"))
+
+for i, frame in enumerate(video):    # frame is (H, W, 3) uint8 RGB
+    row = df.iloc[i]
+    port_pose   = np.asarray(row["labels.port_pose_base"])       # (7,)
+    kp2d_center = np.stack(row["labels.port_keypoints_2d"]["center"])  # (9, 2)
+    visible     = np.asarray(row["labels.port_keypoints_visible"]["center"])  # (9,)
+    # ... build sample
+    if i >= 5:
+        break
+
+# --- camera intrinsics ---
+cam = json.load(open(DS/"assets_snapshot/camera_info.json"))["center"]
+K = np.asarray(cam["K"])                # (3, 3)
+cam_T_tcp = np.asarray(cam["cam_T_tcp"])  # (4, 4) static
+```
+
+### Quality filtering
+
+For imitation learning on clean demonstrations:
+```python
+clean = [e for e in eps if (e.get("success_tier3") or 0) >= 70]
+```
+
+For perception (the label is always correct even on partial trials):
+```python
+usable = eps                              # keep everything
+```
+
+For occlusion-robust training you can drop frames where no camera sees the
+port:
+```python
+vis = np.stack([np.asarray(row["labels.port_keypoints_visible"][c])
+                for c in ("left", "center", "right")])    # (3, 9)
+ok = (vis.sum(axis=1) >= 5).any()         # at least one cam has ≥5/9 visible
+```
+
+### 2D keypoints → 3D port pose (optional PnP)
+
+```python
+import cv2
+kp_local = np.asarray(
+    json.load(open(DS/"assets_snapshot/keypoints_sfp.json"))["points_local"])  # (9, 3)
+kp2d = np.stack(row["labels.port_keypoints_2d"]["center"])                     # (9, 2)
+vis  = np.asarray(row["labels.port_keypoints_visible"]["center"]).astype(bool)
+ok, rvec, tvec = cv2.solvePnP(
+    kp_local[vis], kp2d[vis], K, distCoeffs=np.zeros(5),
+    flags=cv2.SOLVEPNP_ITERATIVE)
+```
+
+## Use cases
+
+- **Port 6D pose regression** — input: one or three camera images, target:
+  `labels.port_pose_base` or `labels.port_keypoints_2d`. The 9-keypoint
+  supervision is compatible with heatmap-style keypoint models and direct
+  regression.
+- **Imitation learning** — use `observation.*` as input history and
+  `action.pose_command` as the target; use `observation.tcp_pose_actual` if
+  you prefer delta actions. Task-condition with `task_index`.
+- **Hybrid policy** — run a learned port-pose estimator on the images, then
+  reuse CheatCode's controller logic with the estimate instead of the GT TF.
+
+## Randomization space (per trial)
+
+Sampled independently from `seed = start_seed + episode_index`.
+
+- **Board pose** — xy jitter ±5 cm around (0.15, −0.2), yaw ±0.5 rad around π.
+- **NIC card (SFP trial)** — exactly one card, target rail uniform on {0..4};
+  translation in [−0.0215, 0.0234] m and yaw in [−10°, +10°]; target port
+  `sfp_port_{0|1}`.
+- **SC port (SC trial)** — exactly one port, target rail 0 or 1, translation in
+  [−0.06, 0.055] m.
+- **Grasp offset** — Gaussian noise around the nominal grasp: σ = 2 mm (xyz)
+  and σ = 0.04 rad (rpy), matching the qualification-spec uncertainty exactly.
+
+## Known quirks
+
+- **Gripper fingers appear slightly open around the plug.** The challenge
+  config sets `attach_cable_to_gripper=True`, which installs a logical fixed
+  joint between the plug and `gripper/tcp` rather than relying on physical
+  grasping, so the finger width doesn't change to match the plug dimensions.
+  This is cosmetic — simulation physics and all labels remain correct.
+- **Frame 0 of every episode is a post-reset settled state**, not the
+  instant the trial started. The collector defers recording until
+  `/joint_states` shows sample-to-sample Δposition under ~0.02 rad for 3
+  consecutive ticks, which skips the one-frame teleport the engine
+  produces when returning the arm to home between trials.
+- **Episode lengths vary** (typically 200–700 frames). Truncation cuts at
+  `argmin(plug→port) + 20` frames so a short stabilisation tail is kept
+  without the full 5 s CheatCode hold or the engine-driven reset motion.
+- **`observation.state.gripper` may be null** for the entire episode if the
+  eval container doesn't publish `/gripper_state`. The gripper width is
+  included in `observation.state.joints` (last element) regardless.
+- **Partial insertions are kept** (`success_tier3 < 70`); filter at train
+  time if your objective needs only clean demonstrations.
+
+## Reproducing a scene
+
+Every `episodes.jsonl` row carries a `seed` and a full `randomization` dict.
+Re-running the sampler with that seed reproduces the exact scene
+configuration (subject to the ROS 2 / Gazebo message-scheduling
+non-determinism noted in the collection README).

assets_snapshot/camera_info.json

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+{
+  "left": {
+    "K": [
+      [
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+        0.0,
+        576.0
+      ],
+      [
+        0.0,
+        1236.6314697265625,
+        512.0
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+      [
+        0.0,
+        0.0,
+        1.0
+      ]
+    ],
+    "D": [
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+      0.0
+    ],
+    "width": 1152,
+    "height": 1024,
+    "distortion_model": "plumb_bob",
+    "frame_id": "left_camera/optical",
+    "cam_T_tcp": [
+      [
+        0.49999939438862684,
+        -0.8660257534267778,
+        -3.548043067069795e-06,
+        -2.1798253317522446e-07
+      ],
+      [
+        0.836515215752322,
+        0.4829625654132389,
+        -0.25882321036628375,
+        0.058939645138193233
+      ],
+      [
+        0.2241492793337801,
+        0.12940848044485037,
+        0.9659247101938648,
+        0.2284762330551336
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        1.0
+      ]
+    ]
+  },
+  "center": {
+    "K": [
+      [
+        1236.6316680908203,
+        0.0,
+        576.0
+      ],
+      [
+        0.0,
+        1236.6314697265625,
+        512.0
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+      [
+        0.0,
+        0.0,
+        1.0
+      ]
+    ],
+    "D": [
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+      0.0
+    ],
+    "width": 1152,
+    "height": 1024,
+    "distortion_model": "plumb_bob",
+    "frame_id": "center_camera/optical",
+    "cam_T_tcp": [
+      [
+        0.999999999993157,
+        -1.0474926010598876e-06,
+        -3.5480430671033133e-06,
+        -8.496835079235735e-07
+      ],
+      [
+        9.34830899709348e-08,
+        0.9659247102003766,
+        -0.2588232103662838,
+        0.05895165634974644
+      ],
+      [
+        3.6982578692114726e-06,
+        0.25882321036418104,
+        0.9659247101938648,
+        0.22848152205751593
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        1.0
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+    ]
+  },
+  "right": {
+    "K": [
+      [
+        1236.6316680908203,
+        0.0,
+        576.0
+      ],
+      [
+        0.0,
+        1236.6314697265625,
+        512.0
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+      [
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+        0.0,
+        1.0
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+    ],
+    "D": [
+      0.0,
+      0.0,
+      0.0,
+      0.0,
+      0.0
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+    "width": 1152,
+    "height": 1024,
+    "distortion_model": "plumb_bob",
+    "frame_id": "right_camera/optical",
+    "cam_T_tcp": [
+      [
+        0.5000012112967422,
+        0.8660247044335408,
+        -3.5480430669726504e-06,
+        -1.481666177906965e-06
+      ],
+      [
+        -0.8365151208202869,
+        0.4829627298402136,
+        -0.2588232103662832,
+        0.05893964513807363
+      ],
+      [
+        -0.22414558068543544,
+        0.12941488668652792,
+        0.9659247101938649,
+        0.2284762330504595
+      ],
+      [
+        0.0,
+        0.0,
+        0.0,
+        1.0
+      ]
+    ]
+  }
+}

assets_snapshot/keypoints_sc.json

@@ -0,0 +1,61 @@
+{
+  "port_type": "sc",
+  "names": [
+    "center",
+    "mouth_tl",
+    "mouth_tr",
+    "mouth_bl",
+    "mouth_br",
+    "base_tl",
+    "base_tr",
+    "base_bl",
+    "base_br"
+  ],
+  "points_local": [
+    [
+      0.0,
+      0.0,
+      0.0
+    ],
+    [
+      -0.011,
+      0.0043,
+      -0.014
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+    [
+      0.011,
+      0.0043,
+      -0.014
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+    [
+      -0.011,
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+      -0.014
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+    [
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+    [
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+      0.0043,
+      0.016
+    ],
+    [
+      0.011,
+      0.0043,
+      0.016
+    ],
+    [
+      -0.011,
+      -0.0043,
+      0.016
+    ],
+    [
+      0.011,
+      -0.0043,
+      0.016
+    ]
+  ]
+}

assets_snapshot/keypoints_sfp.json

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+{
+  "port_type": "sfp",
+  "names": [
+    "center",
+    "mouth_tl",
+    "mouth_tr",
+    "mouth_bl",
+    "mouth_br",
+    "base_tl",
+    "base_tr",
+    "base_bl",
+    "base_br"
+  ],
+  "points_local": [
+    [
+      0.0,
+      0.0,
+      0.0
+    ],
+    [
+      -0.0064,
+      0.0042,
+      -0.0458
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+    [
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+    [
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+      -0.0158
+    ],
+    [
+      0.0064,
+      -0.0042,
+      -0.0158
+    ]
+  ]
+}

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