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Run date: 2026-05-30 Asia/Singapore.
Purpose: show that the committed Ropedia Xperience-10M Task Suite artifacts are
real outputs from the scripts and can be reproduced from the public sample.
## Raw Inputs Checked
The run used the local public sample episode:
```text
data/sample/xperience-10m-sample/
annotation.hdf5
fisheye_cam0.mp4
fisheye_cam1.mp4
fisheye_cam2.mp4
fisheye_cam3.mp4
stereo_left.mp4
stereo_right.mp4
```
`annotation.hdf5` contains 5,821 aligned frames with depth, hand mocap, body
mocap, IMU, SLAM, calibration, and caption metadata. The video feature cache was
rebuilt from all six video files during the run.
## Commands Re-run
All reproduction outputs were written outside the repo:
```bash
REPRO=/path/to/ignored-scratch-workspace
WORKSPACE=/path/to/Ropedia
ANN=$WORKSPACE/data/sample/xperience-10m-sample/annotation.hdf5
PY=$WORKSPACE/.venv/bin/python
$PY -B scripts/train_min_action_model.py \
--workspace $WORKSPACE \
--annotation $ANN \
--output-dir $REPRO/min_action_model \
--target action
$PY -B scripts/train_min_action_model.py \
--workspace $WORKSPACE \
--annotation $ANN \
--output-dir $REPRO/min_subtask_model \
--target subtask
$PY -B scripts/train_all_modalities_model.py \
--workspace $WORKSPACE \
--annotation $ANN \
--output-dir $REPRO/min_all_modalities_action_model \
--cache-dir $REPRO/cache \
--target action
$PY -B scripts/train_all_modalities_model.py \
--workspace $WORKSPACE \
--annotation $ANN \
--output-dir $REPRO/min_all_modalities_subtask_model \
--cache-dir $REPRO/cache \
--target subtask
$PY -B scripts/episode_task_suite.py \
--workspace $WORKSPACE \
--annotation $ANN \
--output-dir $REPRO/episode_task_suite \
--cache-dir $REPRO/cache
```
## Exact Match Checks
The regenerated files matched the committed files:
```text
min_action_model/metrics.json: MATCH
min_subtask_model/metrics.json: MATCH
min_all_modalities_action_model/metrics.json: MATCH
min_all_modalities_subtask_model/metrics.json: MATCH
episode_task_suite/summary_report.json: MATCH
episode_task_suite/feature_manifest.json: MATCH
episode_task_suite/available_modalities.json: MATCH
```
Every per-task `metrics.json` also matched:
```text
caption_grounding/metrics.json: MATCH
contact_prediction/metrics.json: MATCH
cross_modal_retrieval/metrics.json: MATCH
hand_trajectory_forecast/metrics.json: MATCH
misalignment_detection/metrics.json: MATCH
modality_reconstruction/metrics.json: MATCH
next_action/metrics.json: MATCH
object_relevance/metrics.json: MATCH
temporal_order/metrics.json: MATCH
timeline_action/metrics.json: MATCH
timeline_subtask/metrics.json: MATCH
transition_detection/metrics.json: MATCH
```
## Fresh Cache Evidence
The all-modality run rebuilt a fresh feature cache:
```text
depth_n5821_grid8.npz: shape=(5821, 140), nonzero=809107
video_fisheye_cam0_n5821_img32_grid8_hist8.npz: shape=(5821, 98), nonzero=570458
video_fisheye_cam1_n5821_img32_grid8_hist8.npz: shape=(5821, 98), nonzero=570400
video_fisheye_cam2_n5821_img32_grid8_hist8.npz: shape=(5821, 98), nonzero=570458
video_fisheye_cam3_n5821_img32_grid8_hist8.npz: shape=(5821, 98), nonzero=568723
video_stereo_left_n5821_img32_grid8_hist8.npz: shape=(5821, 98), nonzero=570249
video_stereo_right_n5821_img32_grid8_hist8.npz: shape=(5821, 98), nonzero=570430
```
This confirms the committed metrics are reproducible from the raw sample and
that the all-modality pipeline reads real depth/video files instead of using
empty placeholder features.
## Caveats
The scripts contain a zero-feature fallback if a video file is missing. That is
not the path used in this run: all six videos existed and produced nonzero
features. The repo remains a single-episode learning and pipeline-validation
project, not evidence of cross-episode generalization.
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