Remove device distribution and billing information

README.md

@@ -19,7 +19,7 @@ configs:
 
 # RoboX-EgoTask
 
-A premium egocentric dataset of real-world hand and object task demonstrations, recorded in first person through RoboX. This release bundles 17 task clips drawn from 5 full recordings (about 6 minutes of footage), each pairing clean RGB video with synchronized, robotics ready annotations: MediaPipe hand keypoints (21 joints, 2D and 3D), 6DoF camera pose, IMU, depth metadata, body pose, person segmentation, and per frame trajectory and billing signals. It is built for embodied AI and egocentric robotics research, with predefined train, validation and test splits and a motion quality score on every clip.
+A premium egocentric dataset of real-world hand and object task demonstrations, recorded in first person through RoboX. This release bundles 17 task clips drawn from 5 full recordings (about 6 minutes of footage), each pairing clean RGB video with synchronized, robotics ready annotations: MediaPipe hand keypoints (21 joints, 2D and 3D), 6DoF camera pose, IMU, depth metadata, body pose, person segmentation, and per frame trajectory signals. It is built for embodied AI and egocentric robotics research, with predefined train, validation and test splits and a motion quality score on every clip.
 
 ## Examples
 
@@ -220,32 +220,13 @@ Every clip carries a `motion_quality_score` (0–100) derived from device teleme
 
 `manifest.json::quality` carries the dataset-wide tier distribution and mean score so consumers see overall bundle quality before drilling into clips. Missing telemetry (e.g. no light estimate on a legacy device) defaults to a neutral 50 contribution to the composite so older bundles are not penalised relative to fully-equipped captures.
 
-## Billable Duration
-
-The raw video duration in the summary above counts *every* recorded second, including idle reaches, transit footage, and frames where the hand leaves the field of view. Buyers paying per second usually want the **ML-useful** subset:
-
-- `clips.jsonl::billable_frame_count`, `billable_duration_sec`, `billable_percent`: per-clip chargeable subset.
-- `clips.jsonl::billing_rule`: slug of the campaign rule that produced the verdict (full rule lives in `manifest.json::billing.rules_by_data_type`).
-- `clips.jsonl::per_frame_billing_aggregate`: counts of the four per-frame metrics (`hands_visible`, `hands_active`, `object_visible`, `interaction_active`) over the clip.
-- `manifest.json::billing.total_billable_duration_sec` vs `stats.total_duration_sec`: dataset-wide chargeable vs raw timeline.
-
-Each campaign defines its own billing rule:
-
-- **EgoGrasp / EgoDaily**: `interaction_active` (hand + tracked object), billable when a hand is actively grasping or interacting with an object.
-- **EgoNav**: `always_billable`, every recorded frame counts; locomotion data is per-frame useful regardless of hand visibility.
-- **EgoDepth / EgoScene**: `always_billable`, the sensor capture is the product, not hand activity.
-- **EgoSocial**: `hands_or_object_visible`, proxy for social engagement; pure transit footage between encounters is excluded.
-- **EgoHub**: `always_billable`, AI metadata bundle is the product, no per-frame device data is collected.
-
-Raw frame data is preserved in `annotations/billing/<clip>.jsonl` so buyers can re-derive any custom billable predicate (e.g. their own ML-quality filter) without re-running the export.
-
 ## Universal Trajectory Segmentation
 
 In addition to the **campaign-specific** action segments under `annotations/actions/`, every clip carries a **campaign-agnostic** trajectory state stream derived from pose / IMU / Core Motion / hand activity signals. Robotics buyers training cross-campaign models can filter / join by trajectory state directly without ever consulting the campaign-specific taxonomy.
 
 Three states (priority order: `interaction` > `locomotion` > `idle`):
 
-- **`interaction`**: a hand is actively engaged with an object (reuses the Phase 4 billing predicates: `hands_active` or `interaction_active`). Walking-while-holding counts as `interaction`, not `locomotion`.
+- **`interaction`**: a hand is actively engaged with an object (`hands_active` or `interaction_active`). Walking-while-holding counts as `interaction`, not `locomotion`.
 - **`locomotion`**: the body / camera is translating through space. Triggered by linear velocity ≥ 0.2 m/s, sustained IMU user-acceleration ≥ 0.15 m/s², or Core Motion `walking`/`running`/`cycling`/`automotive` activity at non-low confidence.
 - **`idle`**: neither of the above. Hand visible but inactive, body stationary.
 
@@ -278,7 +259,6 @@ This dataset is intended for embodied AI and egocentric robotics research.
 - `hand_keypoints`: 17/17 clips (100.0%)
 - `hand_state`: 17/17 clips (100.0%)
 - `hand_landmarks_mediapipe`: 17/17 clips (100.0%)
-- `billing`: 17/17 clips (100.0%)
 - `trajectory`: 17/17 clips (100.0%)
 - `object_tracks`: 16/17 clips (94.1%)
 - `object_tracks_temporal`: 0/17 clips (0.0%)
@@ -292,7 +272,7 @@ This dataset is intended for embodied AI and egocentric robotics research.
 
 ## Annotation Coverage By Sensor Tier
 
-- `rgbd_full` (17 clips): actions 17/17 (100.0%), billing 17/17 (100.0%), body_pose 2/17 (11.8%), environment 17/17 (100.0%), hand_keypoints 17/17 (100.0%), hand_state 17/17 (100.0%), object_tracks 16/17 (94.1%), people 16/17 (94.1%), segmentation 16/17 (94.1%), sensors 17/17 (100.0%), trajectory 17/17 (100.0%)
+- `rgbd_full` (17 clips): actions 17/17 (100.0%), body_pose 2/17 (11.8%), environment 17/17 (100.0%), hand_keypoints 17/17 (100.0%), hand_state 17/17 (100.0%), object_tracks 16/17 (94.1%), people 16/17 (94.1%), segmentation 16/17 (94.1%), sensors 17/17 (100.0%), trajectory 17/17 (100.0%)
 
 ## Dataset Splits
 
@@ -308,11 +288,6 @@ This dataset is intended for embodied AI and egocentric robotics research.
 
 - No domain-level distribution available
 
-## Device Distribution
-
-- `iPhone 13`: 4
-- `iPhone 11`: 1
-
 ## Structure
 
 - `recordings/`: Clean RGB full recordings (training-safe; no baked hand overlays).
@@ -332,7 +307,6 @@ This dataset is intended for embodied AI and egocentric robotics research.
 - `annotations/hand_keypoints/`: Per-frame hand pose (21 joints). **As of schema v1.5** this stream is sourced from MediaPipe HandLandmarker running in the export backend, see `manifest.json::mediapipe_hands` and `clips.jsonl::hand_detection_source` for provenance. The on-device iOS Vision hands are still captured by the app but are no longer the canonical source in the export bundle; this guarantees left/right chirality is consistent across all annotation streams.
 - `annotations/hand_landmarks_mediapipe/`: Per-**video**-frame raw MediaPipe stream (one entry per frame at video fps, including frames where no hand was detected). Finer rate than `hand_keypoints` (which samples at iOS metadata rate, ~10 Hz). Same field shape as `hand_keypoints` entries.
 - `annotations/hand_state/`: Per-frame bimanual state (left/right visible + active flags, per-hand grip, per-hand `in_hand`). Always rides along with `hand_keypoints`.
-- `annotations/billing/`: Per-frame billing metrics (`hands_visible`, `hands_active`, `object_visible`, `interaction_active`) plus the campaign rule's `billable` verdict. Emitted for every clip with per-frame data.
 - `annotations/trajectory/`: Per-frame **universal** robotics trajectory state (`idle` / `locomotion` / `interaction`) plus the raw signals that produced it (linear velocity, IMU acceleration, Core Motion activity). Campaign-agnostic and orthogonal to the campaign-specific actions stored in `annotations/actions/`.
 - `annotations/object_tracks/`: Per-frame object bounding boxes
 - `annotations/sensors/`: IMU, 6DoF camera pose, intrinsics, plus a per-frame `depth` block (`available`, `resolution`, `stats`, `intrinsics`), full LiDAR/TrueDepth metadata when the device captured it (Pro+ tier)