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"title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
"status": "pass",
"version": "2026-06-01",
"generated_at_utc": "2026-06-03T12:47:15+00:00",
"source_files": [
"docs/data/summary_metrics.json",
"results/episode_task_suite/summary_report.json",
"results/episode_task_suite/windows.csv",
"results/episode_task_suite/feature_manifest.json"
],
"scope": {
"validated_episode_count": 1,
"annotation": "data/sample/xperience-10m-sample/annotation.hdf5",
"num_frames": 5821,
"num_windows": 1161,
"feature_dim": 8546,
"window_frames": 20,
"stride_frames": 5,
"audio_featurized": true,
"raw_data_redistributed": false
},
"split_policy": {
"name": "single_episode_chronological",
"train_fraction": 0.7,
"test_fraction": 0.3,
"why": "The split preserves time order so future episode segments are not mixed randomly into the train set.",
"limitation": "It is still one episode; cross-episode generalization is evaluated in the multi-episode stage."
},
"feature_policy": {
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"source_manifest": "results/episode_task_suite/feature_manifest.json",
"normalization": "Scalers are fit on train windows only for the baseline heads.",
"audio_status": "Audio is one of the synchronized source modalities in the current task representation."
},
"baselines": [
{
"name": "minimal",
"heads": [
"softmax",
"binary logistic",
"multi-label logistic",
"ridge regression",
"ridge projection plus cosine ranking"
],
"purpose": "Keep each task contract interpretable and easy to debug."
},
{
"name": "neural_mlp",
"heads": [
"PyTorch MLP classifier",
"PyTorch MLP regressor",
"PyTorch MLP multi-label head"
],
"purpose": "Check nonlinear gains before larger omni-model fine-tuning.",
"config": {
"name": "neural_mlp",
"type": "lightweight PyTorch MLP over shared window features",
"epochs": 80,
"hidden_dim": 128,
"batch_size": 128,
"learning_rate": 0.001,
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"dropout": 0.1,
"device": "auto"
}
}
],
"task_protocols": [
{
"task": "timeline_action",
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"unit": "single window",
"input": "current 20-frame all-feature window",
"target": "current action label",
"primary_metric": "macro_f1",
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},
{
"task": "timeline_subtask",
"family": "supervised classification",
"unit": "single window",
"input": "current 20-frame all-feature window",
"target": "current subtask label",
"primary_metric": "macro_f1",
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},
{
"task": "transition_detection",
"family": "temporal diagnostic",
"unit": "single window",
"input": "current 20-frame all-feature window",
"target": "action boundary versus steady",
"primary_metric": "macro_f1",
"higher_is_better": true,
"leakage_rule": "Boundary labels are targets only. Boundary timing is evaluated after prediction.",
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},
{
"task": "next_action",
"family": "short-horizon prediction",
"unit": "single window",
"input": "current 20-frame all-feature window at time t",
"target": "action label at t + 20 frames",
"primary_metric": "macro_f1",
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},
{
"task": "hand_trajectory_forecast",
"family": "trajectory regression",
"unit": "single window",
"input": "current all-feature window",
"target": "future left/right hand 3D joints for 10 frames",
"primary_metric": "mpjpe",
"higher_is_better": false,
"leakage_rule": "Future mocap coordinates are targets only, not inputs.",
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},
{
"task": "contact_prediction",
"family": "binary classification",
"unit": "single window",
"input": "non-contact and non-caption signals",
"target": "any body contact",
"primary_metric": "macro_f1",
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"neural_metric_source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json"
},
{
"task": "object_relevance",
"family": "multi-label classification",
"unit": "single window",
"input": "non-caption signals",
"target": "current relevant object set",
"primary_metric": "micro_f1",
"higher_is_better": true,
"leakage_rule": "Caption/object-label fields are excluded from inputs.",
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},
{
"task": "caption_grounding",
"family": "retrieval",
"unit": "caption query",
"input": "caption object/interaction query plus candidate sensor windows",
"target": "matching time window",
"primary_metric": "mrr",
"higher_is_better": true,
"leakage_rule": "Queries are ranked against held-out candidate windows; reported ranks are computed after model scoring.",
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"neural_metric_source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json"
},
{
"task": "cross_modal_retrieval",
"family": "retrieval",
"unit": "sensor query",
"input": "motion, IMU, and camera query features",
"target": "matching depth/video window",
"primary_metric": "top5_accuracy",
"higher_is_better": true,
"leakage_rule": "Query-side and candidate-side signals are split before projection/ranking.",
"counts": {
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"neural_metric_source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json"
},
{
"task": "modality_reconstruction",
"family": "cross-modal regression",
"unit": "single window",
"input": "motion, IMU, and camera features",
"target": "depth/video feature vector",
"primary_metric": "r2",
"higher_is_better": true,
"leakage_rule": "Target-side signals are excluded from the input side.",
"counts": {
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"neural_metric_source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json"
},
{
"task": "temporal_order",
"family": "pairwise diagnostic",
"unit": "adjacent window pair",
"input": "two adjacent windows",
"target": "correct versus reversed order",
"primary_metric": "f1",
"higher_is_better": true,
"leakage_rule": "Pairs are built after windowing; labels are synthetic order labels, not input features.",
"counts": {
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"num_train_samples": 1624,
"num_test_samples": 696
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"minimal_metric_source": "results/episode_task_suite/temporal_order/metrics.json",
"neural_metric_source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json"
},
{
"task": "misalignment_detection",
"family": "pairwise diagnostic",
"unit": "paired modality window",
"input": "motion side plus visual/depth side",
"target": "aligned versus shifted by 8 windows",
"primary_metric": "f1",
"higher_is_better": true,
"leakage_rule": "Shift labels are synthetic targets; shifted visual/depth blocks are generated after feature splitting.",
"counts": {
"num_samples": 2306,
"num_train_samples": 1614,
"num_test_samples": 692
},
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"neural_metric_source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json"
}
],
"global_leakage_controls": [
"Use chronological train/test splits instead of random window shuffling.",
"Fit scalers and learned projections on train windows only.",
"Keep future labels, future mocap, contact labels, object labels, and caption labels on the target side unless a task explicitly treats language as the query.",
"For cross-modal tasks, split query-side and candidate-side signals before training and ranking.",
"Report unseen test classes when the chronological split exposes labels absent from the train segment."
],
"current_limitations": [
"Cross-episode generalization is evaluated in the later multi-episode stage.",
"Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.",
"Qwen3-Omni setup artifacts are preparation artifacts until the selected held-out pilot runs.",
"Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations."
],
"scale_up_gate": {
"required_before_full_omni_pilot": [
"selected staged Xperience-10M episodes",
"held-out episode split with no train/test episode leakage",
"manifest, training metadata, progress logs, metrics, predictions, and run report",
"held-out evaluation on test episodes rather than train windows"
],
"current_status": "prepared; selected data relay in progress",
"evidence": [
"results/omni_finetune/DATA_ACCESS_STATUS.md",
"results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md"
]
}
}
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