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<title>Ropedia Episode Task Suite</title>
<meta name="description" content="A transparent multimodal task suite for one public Ropedia / Xperience-10M sample episode.">
<meta property="og:title" content="Ropedia Episode Task Suite">
<meta property="og:description" content="One public embodied-AI episode, converted into reproducible multimodal tasks, baselines, metrics, and diagrams.">
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<a class="brand" href="#top" aria-label="Ropedia Episode Task Suite home">
<span class="mark" aria-hidden="true">R</span>
<span>Ropedia Episode Task Suite</span>
</a>
<div class="nav-links" aria-label="Page navigation">
<a href="#suite">Suite</a>
<a href="#pipeline">Pipeline</a>
<a href="#models">Models</a>
<a href="#architectures">Arch</a>
<a href="#tasks">Tasks</a>
<a href="#features">Signals</a>
<a href="#artifacts">Artifacts</a>
<a class="nav-action" href="https://huggingface.co/spaces/cy0307/ropedia-episode-task-suite">HF Space</a>
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<header class="hero" id="top">
<div class="wrap hero-inner">
<div>
<div class="eyebrow">single public episode / verified artifacts</div>
<h1>One Ropedia episode, made auditable.</h1>
<p class="hero-copy">
A compact research lab for the public Ropedia / Xperience-10M sample:
video, audio, depth, pose, motion capture, inertial sensing, and language annotation,
with an explicit record of which modalities enter the current minimal feature vector.
</p>
<div class="hero-actions">
<a class="button primary" href="#suite">Inspect 12 tasks</a>
<a class="button" href="https://cy0307-ropedia-episode-task-suite.static.hf.space/">Open HF app</a>
<a class="button" href="https://github.com/ChaoYue0307/ropedia-episode-task-suite">View repository</a>
<a class="button" href="data/summary_metrics.json">Read metrics JSON</a>
</div>
<div class="hero-stats">
<div class="stat"><strong>5,821</strong><span>frames in sample episode</span></div>
<div class="stat"><strong>1,161</strong><span>20-frame windows</span></div>
<div class="stat"><strong>8,378</strong><span>current feature dimensions</span></div>
<div class="stat"><strong>12</strong><span>end-to-end task definitions</span></div>
</div>
</div>
<div class="hero-panel" aria-label="Signal summary">
<div class="panel-top">
<span>current feature allocation</span>
<span>window vector</span>
</div>
<div class="signal"><code>mocap</code><div class="track"><span style="--w:25.3%;--c:#1f6c9f"></span></div><strong>2,121</strong></div>
<div class="signal"><code>camera+imu</code><div class="track"><span style="--w:1.5%;--c:#2e7775"></span></div><strong>126</strong></div>
<div class="signal"><code>depth</code><div class="track"><span style="--w:11.7%;--c:#346538"></span></div><strong>980</strong></div>
<div class="signal"><code>video</code><div class="track"><span style="--w:49.1%;--c:#956400"></span></div><strong>4,116</strong></div>
<div class="signal"><code>language</code><div class="track"><span style="--w:10.7%;--c:#6f5b21"></span></div><strong>896</strong></div>
<div class="signal"><code>static</code><div class="track"><span style="--w:1.7%;--c:#6f716c"></span></div><strong>139</strong></div>
</div>
</div>
</header>
<main id="main">
<section id="suite">
<div class="wrap">
<div class="section-head">
<h2>Ropedia 12-task suite, first.</h2>
<p>The top-level map shows the Xperience-10M sample modalities and the verified 12-task results. Audio is present in the sample MP4 stream, but the current 8,378-d baseline manifest does not featurize it.</p>
</div>
<img class="task-suite-image" src="assets/task_suite_infographic.png?v=bb2beb9" alt="ChatGPT-image-backed infographic showing all 12 Ropedia episode tasks">
</div>
</section>
<section id="pipeline">
<div class="wrap">
<div class="section-head">
<h2>From raw episode to checked artifacts.</h2>
<p>Every script works from one data contract: aligned multimodal windows, explicit labels, cached feature extraction, and a manifest that makes omitted modalities visible.</p>
</div>
<img class="pipeline-image" src="assets/pipeline_diagram.png?v=bb2beb9" alt="Verified Ropedia multimodal pipeline diagram">
<div class="callout-row">
<div class="callout">
<h3>What this project proves</h3>
<p>It proves the full engineering loop: reading Ropedia sample data, aligning modalities, converting them into model-ready windows, defining meaningful tasks, producing metrics, and packaging every artifact for review.</p>
</div>
<div class="callout">
<h3>What it does not prove</h3>
<p>It does not claim general embodied intelligence. A single episode cannot support cross-environment generalization; that requires many episodes and held-out episode splits.</p>
</div>
</div>
</div>
</section>
<section id="models">
<div class="wrap">
<div class="section-head">
<h2>Small baselines, no hidden machinery.</h2>
<p>Motion-only and current all-feature classifiers use lightweight heads so the comparison stays readable on a laptop and easy to audit.</p>
</div>
<div class="models">
<article class="model"><h3>Motion-only action</h3><span class="score">0.9688</span><span class="meta">macro-F1, 18 classes</span></article>
<article class="model"><h3>Current all-feature action</h3><span class="score">0.9791</span><span class="meta">macro-F1, 8,378 features</span></article>
<article class="model"><h3>Motion-only subtask</h3><span class="score">0.9528</span><span class="meta">macro-F1, 14 classes</span></article>
<article class="model"><h3>Current all-feature subtask</h3><span class="score">0.9308</span><span class="meta">macro-F1, chronological caveats</span></article>
</div>
<img class="chart" src="assets/charts/model_macro_f1.svg" alt="Macro-F1 comparison chart">
</div>
</section>
<section id="architectures">
<div class="wrap">
<div class="section-head">
<h2>The 12 tasks share four head families.</h2>
<p>The diagram separates the shared episode-window feature pipeline from the task-specific heads, and notes that audio remains dataset context rather than a current baseline feature block.</p>
</div>
<img class="architecture-image" src="assets/task_architectures.png?v=bb2beb9" alt="Verified minimal architecture diagram for all 12 Ropedia episode tasks">
</div>
</section>
<section id="tasks">
<div class="wrap">
<div class="section-head">
<h2>Task cards and metrics.</h2>
<p>The same 12 tasks are kept filterable here so the supervised, forecast, retrieval, and diagnostic probes can be inspected individually.</p>
</div>
<div class="task-toolbar" aria-label="Task filters">
<button class="filter active" data-filter="all">All tasks</button>
<button class="filter" data-filter="supervised">Supervised</button>
<button class="filter" data-filter="forecast">Forecast</button>
<button class="filter" data-filter="retrieval">Retrieval</button>
<button class="filter" data-filter="diagnostic">Diagnostic</button>
</div>
<div class="task-grid" id="taskGrid">
<article class="task-card" data-kind="supervised"><div class="task-top"><span class="task-name">timeline_action</span><span class="tag supervised">supervised</span></div><p>All featurized modalities to current action label. Chronological split exposes unseen future actions.</p><div class="metric-row"><span>macro-F1</span><strong>0.0500</strong></div><div class="mini-bar"><span style="--w:5%;--c:#1f6c9f"></span></div></article>
<article class="task-card" data-kind="supervised"><div class="task-top"><span class="task-name">timeline_subtask</span><span class="tag supervised">supervised</span></div><p>All featurized modalities to current subtask label. Useful for segmentation diagnostics.</p><div class="metric-row"><span>macro-F1</span><strong>0.0495</strong></div><div class="mini-bar"><span style="--w:5%;--c:#1f6c9f"></span></div></article>
<article class="task-card" data-kind="diagnostic"><div class="task-top"><span class="task-name">transition_detection</span><span class="tag diagnostic">diagnostic</span></div><p>Predict steady vs action boundary. Highlights task-transition localization quality.</p><div class="metric-row"><span>macro-F1</span><strong>0.6552</strong></div><div class="mini-bar"><span style="--w:65.5%;--c:#956400"></span></div></article>
<article class="task-card" data-kind="supervised"><div class="task-top"><span class="task-name">next_action</span><span class="tag supervised">supervised</span></div><p>Current multimodal window to action 20 frames later. Tests short-horizon task flow.</p><div class="metric-row"><span>macro-F1</span><strong>0.0593</strong></div><div class="mini-bar"><span style="--w:5.9%;--c:#1f6c9f"></span></div></article>
<article class="task-card" data-kind="forecast"><div class="task-top"><span class="task-name">hand_trajectory_forecast</span><span class="tag forecast">forecast</span></div><p>Predict future left/right hand 3D joints. Closer to imitation-learning style signals.</p><div class="metric-row"><span>MPJPE</span><strong>0.8223</strong></div><div class="mini-bar"><span style="--w:48%;--c:#346538"></span></div></article>
<article class="task-card" data-kind="supervised"><div class="task-top"><span class="task-name">contact_prediction</span><span class="tag supervised">supervised</span></div><p>Non-contact modalities to binary contact. Degenerate in this sample because one class dominates.</p><div class="metric-row"><span>accuracy</span><strong>1.0000</strong></div><div class="mini-bar"><span style="--w:100%;--c:#346538"></span></div></article>
<article class="task-card" data-kind="supervised"><div class="task-top"><span class="task-name">object_relevance</span><span class="tag supervised">supervised</span></div><p>Predict relevant object set from non-caption feature blocks.</p><div class="metric-row"><span>micro-F1</span><strong>0.1839</strong></div><div class="mini-bar"><span style="--w:18.4%;--c:#1f6c9f"></span></div></article>
<article class="task-card" data-kind="retrieval"><div class="task-top"><span class="task-name">caption_grounding</span><span class="tag retrieval">retrieval</span></div><p>Caption objects/interaction query to matching sensor window.</p><div class="metric-row"><span>MRR</span><strong>0.0172</strong></div><div class="mini-bar"><span style="--w:1.7%;--c:#2e7775"></span></div></article>
<article class="task-card" data-kind="retrieval"><div class="task-top"><span class="task-name">cross_modal_retrieval</span><span class="tag retrieval">retrieval</span></div><p>Motion/IMU/camera query retrieves matching depth/video window. Strongest single-episode signal.</p><div class="metric-row"><span>top-5</span><strong>0.3764</strong></div><div class="mini-bar"><span style="--w:37.6%;--c:#2e7775"></span></div></article>
<article class="task-card" data-kind="forecast"><div class="task-top"><span class="task-name">modality_reconstruction</span><span class="tag forecast">forecast</span></div><p>Motion/IMU/camera to depth/video feature vector.</p><div class="metric-row"><span>R2</span><strong>-0.0160</strong></div><div class="mini-bar"><span style="--w:2%;--c:#9f2f2d"></span></div></article>
<article class="task-card" data-kind="diagnostic"><div class="task-top"><span class="task-name">temporal_order</span><span class="tag diagnostic">diagnostic</span></div><p>Two adjacent windows to correct vs reversed order.</p><div class="metric-row"><span>F1</span><strong>0.5487</strong></div><div class="mini-bar"><span style="--w:54.9%;--c:#956400"></span></div></article>
<article class="task-card" data-kind="diagnostic"><div class="task-top"><span class="task-name">misalignment_detection</span><span class="tag diagnostic">diagnostic</span></div><p>Motion+visual pair to aligned vs shifted by eight windows.</p><div class="metric-row"><span>F1</span><strong>0.4866</strong></div><div class="mini-bar"><span style="--w:48.7%;--c:#956400"></span></div></article>
</div>
</div>
</section>
<section id="features">
<div class="wrap">
<div class="section-head">
<h2>Every feature block has a source.</h2>
<p>The point is not hidden complexity. Every block has a source modality, a dimensional footprint, and a manifest entry.</p>
</div>
<img class="chart" src="assets/charts/feature_blocks.svg" alt="All modality feature block chart">
</div>
</section>
<section id="diagnostics">
<div class="wrap">
<div class="section-head">
<h2>Diagnostics separate memorization from signal.</h2>
<p>The charts make the main lesson visible: within-episode supervised labels are easy under some splits, while retrieval, grounding, forecasting, and alignment remain the useful probes.</p>
</div>
<div class="chart-grid">
<img class="chart" src="assets/charts/episode_task_scores.svg" alt="Episode task suite score chart">
<img class="chart" src="assets/charts/cross_modal_retrieval.svg" alt="Cross modal retrieval chart">
</div>
</div>
</section>
<section id="artifacts">
<div class="wrap">
<div class="section-head">
<h2>Where the evidence lives.</h2>
<p>Metrics, predictions, confusion matrices, manifests, model weights, and derived window artifacts are committed so the repo is reviewable before rerunning anything.</p>
</div>
<div class="artifact-grid">
<article class="artifact"><h3>Task-suite report</h3><p>One JSON file with every task metric and split detail.</p><a href="https://github.com/ChaoYue0307/ropedia-episode-task-suite/blob/main/results/episode_task_suite/summary_report.json">summary_report.json</a></article>
<article class="artifact"><h3>Feature manifest</h3><p>Start/end index and dimension for every current feature block.</p><a href="https://github.com/ChaoYue0307/ropedia-episode-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature_manifest.json</a></article>
<article class="artifact"><h3>Cross-modal retrieval</h3><p>The strongest self-supervised signal from the single episode.</p><a href="https://github.com/ChaoYue0307/ropedia-episode-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">metrics.json</a></article>
<article class="artifact"><h3>Current all-feature action model</h3><p>Classifier metrics, predictions, confusion matrix, and model weights.</p><a href="https://github.com/ChaoYue0307/ropedia-episode-task-suite/blob/main/results/min_all_modalities_action_model/metrics.json">metrics.json</a></article>
<article class="artifact"><h3>Windows table</h3><p>Window start/end frames and aligned action/subtask labels.</p><a href="https://github.com/ChaoYue0307/ropedia-episode-task-suite/blob/main/results/episode_task_suite/windows.csv">windows.csv</a></article>
<article class="artifact"><h3>Reproduction scripts</h3><p>Three training scripts plus the dashboard generator.</p><a href="https://github.com/ChaoYue0307/ropedia-episode-task-suite/tree/main/scripts">scripts/</a></article>
<article class="artifact"><h3>Hugging Face Space</h3><p>The same dashboard packaged as a public static Space.</p><a href="https://huggingface.co/spaces/cy0307/ropedia-episode-task-suite">cy0307/ropedia-episode-task-suite</a></article>
<article class="artifact"><h3>Derived HF artifacts</h3><p>Metrics, predictions, docs, and lightweight derived files without raw Ropedia video/data redistribution.</p><a href="https://huggingface.co/datasets/cy0307/ropedia-episode-task-suite-artifacts">dataset repo</a></article>
<article class="artifact"><h3>HF baseline models</h3><p>Minimal NumPy softmax and ridge baseline weights with model card and architecture diagrams.</p><a href="https://huggingface.co/cy0307/ropedia-minimal-task-baselines">model repo</a></article>
<article class="artifact"><h3>HF collection</h3><p>Space, artifacts, and model baselines grouped into one public project collection.</p><a href="https://huggingface.co/collections/cy0307/ropedia-episode-task-suite">collection</a></article>
</div>
</div>
</section>
<section id="run">
<div class="wrap">
<div class="section-head">
<h2>Reproduce the suite.</h2>
<p>Raw Ropedia data is not redistributed here. Download the public sample separately, then run the same scripts.</p>
</div>
<pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git
python3.12 -m venv .venv
source .venv/bin/activate
pip install -r HOMIE-toolkit/requirements.txt huggingface_hub hf_xet
hf download ropedia-ai/xperience-10m-sample \
--repo-type dataset \
--local-dir data/sample/xperience-10m-sample
git clone https://github.com/ChaoYue0307/ropedia-episode-task-suite.git
cd ropedia-episode-task-suite
python scripts/episode_task_suite.py --workspace /path/to/workspace</code></pre>
</div>
</section>
</main>
<footer>
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Built as a single-episode embodied-AI learning lab. For real model claims, scale to multiple episodes and hold out entire episodes at evaluation time.
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