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Download docs/index.html from cy0307/ropedia-xperience-10m-task-suite-artifacts: direct link, hf CLI and curl.
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https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/11e071dc9a3bcfe32c4523d85d21c5bd990c2a42/docs/index.html
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hf download hf://datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts@11e071dc9a3bcfe32c4523d85d21c5bd990c2a42/docs/index.html
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curl -L -o index.html https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/11e071dc9a3bcfe32c4523d85d21c5bd990c2a42/docs/index.html
147 kB
| <html lang="en"> | |
| <head> | |
| <meta charset="utf-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1"> | |
| <title>Ropedia Xperience-10M Task Suite</title> | |
| <meta name="description" content="A research-development task lab for Ropedia Xperience-10M: multimodal sample exploration, 12 embodied-AI tasks, baseline models, and a multi-episode fine-tuning path."> | |
| <meta name="theme-color" content="#020502"> | |
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| <meta property="og:description" content="A Ropedia Xperience-10M research task lab with multimodal sample exploration, 12 task contracts, minimal and neural baselines, metrics, diagrams, and a scale-up plan."> | |
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| <meta name="twitter:title" content="Ropedia Xperience-10M Task Suite"> | |
| <meta name="twitter:description" content="Xperience-10M research task lab with multimodal sample exploration, 12 task contracts, and minimal plus neural baselines."> | |
| <meta name="twitter:image" content="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/brand/xperience10m-logo-social-card.png?v=xperience10m-logo-v1"> | |
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| linear-gradient(180deg, rgba(164, 242, 127, 0.08), rgba(7, 18, 7, 0.88)), | |
| var(--surface); | |
| padding: clamp(26px, 3.2vw, 44px); | |
| box-shadow: 0 18px 46px rgba(0, 0, 0, 0.28); | |
| } | |
| .atlas-head { | |
| display: flex; | |
| align-items: end; | |
| justify-content: space-between; | |
| gap: 24px; | |
| margin-bottom: 18px; | |
| padding-bottom: 16px; | |
| border-bottom: 1px solid var(--soft-line); | |
| } | |
| .atlas-head h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(24px, 3vw, 36px); | |
| line-height: 1.05; | |
| } | |
| .atlas-head p { | |
| margin: 8px 0 0; | |
| max-width: 780px; | |
| color: var(--muted); | |
| font-size: 15px; | |
| line-height: 1.55; | |
| } | |
| .atlas-head a { | |
| flex: none; | |
| color: var(--cyan); | |
| font-size: 13px; | |
| font-weight: 760; | |
| text-decoration: none; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| padding: 9px 10px; | |
| background: rgba(2, 5, 2, 0.44); | |
| } | |
| .atlas-head a:hover { border-color: var(--green); color: var(--ink); } | |
| .modality-atlas { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(420px, 1fr)); | |
| gap: clamp(24px, 2.4vw, 36px); | |
| } | |
| .atlas-card { | |
| min-width: 0; | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| background: rgba(2, 9, 2, 0.84); | |
| padding: clamp(24px, 2.4vw, 34px); | |
| display: grid; | |
| gap: 24px; | |
| align-content: start; | |
| } | |
| .atlas-card.wide { grid-column: 1 / -1; } | |
| .atlas-card img { | |
| width: 100%; | |
| aspect-ratio: 16 / 9; | |
| object-fit: cover; | |
| display: block; | |
| border: 1px solid rgba(164, 242, 127, 0.16); | |
| border-radius: 8px; | |
| background: #020502; | |
| } | |
| .atlas-card.wide img { | |
| aspect-ratio: 1500 / 470; | |
| } | |
| .atlas-top { | |
| display: flex; | |
| align-items: start; | |
| justify-content: space-between; | |
| gap: 16px; | |
| } | |
| .atlas-index { | |
| display: block; | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 15px; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .atlas-card h4 { | |
| margin: 6px 0 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(38px, 4.2vw, 64px); | |
| line-height: 0.96; | |
| text-transform: uppercase; | |
| } | |
| .atlas-type { | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 13.5px; | |
| line-height: 1.2; | |
| text-align: right; | |
| text-transform: uppercase; | |
| } | |
| .atlas-rows { | |
| display: grid; | |
| grid-template-columns: 1fr; | |
| gap: 0; | |
| } | |
| .atlas-row { | |
| display: grid; | |
| grid-template-columns: minmax(156px, 0.32fr) minmax(0, 1fr); | |
| gap: 22px; | |
| align-items: baseline; | |
| border-top: 1px solid var(--soft-line); | |
| padding: 13px 0 0; | |
| min-width: 0; | |
| } | |
| .atlas-row + .atlas-row { margin-top: 12px; } | |
| .atlas-row span { | |
| display: block; | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 12.5px; | |
| line-height: 1.35; | |
| text-transform: uppercase; | |
| } | |
| .atlas-row p { | |
| margin: 0; | |
| color: #edf8e8; | |
| font-size: clamp(21px, 1.85vw, 28px); | |
| font-weight: 720; | |
| line-height: 1.18; | |
| } | |
| .atlas-card.audio-card { | |
| border-color: rgba(216, 244, 165, 0.32); | |
| background: | |
| linear-gradient(180deg, rgba(216, 244, 165, 0.08), rgba(2, 9, 2, 0.88)), | |
| rgba(2, 9, 2, 0.84); | |
| } | |
| .atlas-card.audio-card .atlas-type { color: var(--amber); } | |
| .atlas-note { | |
| margin: 16px 0 0; | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .architecture-image { | |
| display: block; | |
| } | |
| .callout-row { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| margin-top: 18px; | |
| } | |
| .two-col { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr) minmax(340px, 0.52fr); | |
| gap: 30px; | |
| align-items: start; | |
| } | |
| .callout { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 24px; | |
| background: rgba(164, 242, 127, 0.06); | |
| color: #eaf5e5; | |
| } | |
| .callout h3, .artifact h3 { margin: 0 0 8px; font-size: 17px; } | |
| .callout p, .artifact p { margin: 0; color: #aab5a5; line-height: 1.6; } | |
| .snapshot-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 16px; | |
| } | |
| .snapshot-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 20px; | |
| background: | |
| linear-gradient(180deg, rgba(164, 242, 127, 0.08), rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| min-height: 210px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .snapshot-card.gated { | |
| border-color: rgba(216, 244, 165, 0.32); | |
| background: | |
| linear-gradient(180deg, rgba(216, 244, 165, 0.075), rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| } | |
| .snapshot-card h3 { margin: 0; font-size: 18px; line-height: 1.2; } | |
| .snapshot-card p { margin: 0; color: var(--muted); line-height: 1.58; } | |
| .snapshot-meta { | |
| display: grid; | |
| gap: 6px; | |
| margin-top: 2px; | |
| padding-top: 12px; | |
| border-top: 1px solid var(--soft-line); | |
| color: #dce8d6; | |
| font-size: 13px; | |
| } | |
| .snapshot-meta span { | |
| display: flex; | |
| justify-content: space-between; | |
| gap: 14px; | |
| color: var(--muted); | |
| } | |
| .snapshot-meta strong { | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .snapshot-actions { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 20px; | |
| } | |
| .snapshot-actions a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 13px; | |
| font-weight: 760; | |
| padding: 9px 10px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .snapshot-actions a:hover { border-color: var(--green); color: var(--ink); } | |
| .reading-grid { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-bottom: 18px; | |
| } | |
| .reading-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: | |
| linear-gradient(180deg, rgba(164, 242, 127, 0.075), rgba(7, 18, 7, 0.9)), | |
| var(--surface); | |
| min-height: 270px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .reading-card .step-index { | |
| width: 38px; | |
| height: 38px; | |
| display: grid; | |
| place-items: center; | |
| border: 1px solid rgba(164, 242, 127, 0.36); | |
| border-radius: 8px; | |
| color: #020502; | |
| background: var(--green); | |
| font-family: var(--font-mono); | |
| font-weight: 800; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .reading-card h3 { margin: 0; font-size: 17px; line-height: 1.22; } | |
| .reading-card p { margin: 0; color: var(--muted); font-size: 13px; line-height: 1.55; } | |
| .reading-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 8px; | |
| margin-top: 4px; | |
| } | |
| .reading-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--cyan); | |
| font-size: 12px; | |
| font-weight: 740; | |
| padding: 7px 8px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .reading-links a:hover { border-color: var(--green); color: var(--ink); } | |
| .boundary-strip { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-top: 18px; | |
| } | |
| .boundary-item { | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: rgba(164, 242, 127, 0.055); | |
| } | |
| .boundary-item strong { | |
| display: block; | |
| margin-bottom: 8px; | |
| color: var(--ink); | |
| font-size: 15px; | |
| } | |
| .boundary-item span { | |
| color: var(--muted); | |
| font-size: 13px; | |
| line-height: 1.55; | |
| } | |
| .evidence-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| } | |
| .evidence-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 22px; | |
| background: | |
| linear-gradient(180deg, rgba(164, 242, 127, 0.06), rgba(7, 18, 7, 0.88)), | |
| var(--surface); | |
| min-height: 230px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .evidence-card h3 { margin: 0; font-size: 18px; line-height: 1.2; } | |
| .evidence-card p { margin: 0; color: var(--muted); line-height: 1.6; } | |
| .evidence-card code { color: var(--ink); font-family: var(--font-mono); font-size: 12px; } | |
| .evidence-card:last-child { grid-column: 1 / -1; } | |
| .evidence-links { | |
| display: flex; | |
| flex-wrap: wrap; | |
| gap: 10px; | |
| margin-top: 4px; | |
| } | |
| .evidence-links a { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| color: var(--blue); | |
| font-size: 13px; | |
| font-weight: 740; | |
| padding: 7px 9px; | |
| text-decoration: none; | |
| background: rgba(2, 5, 2, 0.42); | |
| } | |
| .evidence-links a:hover { border-color: var(--green); color: var(--ink); } | |
| .models { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-bottom: 24px; | |
| } | |
| .model { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| background: var(--surface); | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), box-shadow 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .model:hover { transform: translateY(-3px); box-shadow: 0 18px 38px rgba(164, 242, 127, 0.08); } | |
| .model h3 { margin: 0; font-size: 15px; } | |
| .model .score { display: block; margin-top: 18px; font-family: var(--font-mono); font-size: 33px; font-weight: 760; line-height: 1; font-variant-numeric: tabular-nums; } | |
| .model .meta { display: block; margin-top: 8px; color: var(--muted); font-size: 13px; } | |
| .task-toolbar { | |
| display: flex; | |
| gap: 10px; | |
| flex-wrap: wrap; | |
| margin-bottom: 18px; | |
| } | |
| .filter { | |
| border: 1px solid var(--line); | |
| background: var(--surface); | |
| border-radius: 999px; | |
| height: 36px; | |
| padding: 0 14px; | |
| font-weight: 650; | |
| color: #d7e3d0; | |
| cursor: pointer; | |
| transition: transform 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .filter:hover { transform: translateY(-1px); } | |
| .filter.active { color: #020502; background: var(--green); border-color: var(--green); } | |
| .task-grid { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 18px; | |
| } | |
| .task-card { | |
| appearance: none; | |
| width: 100%; | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| padding: 20px; | |
| background: var(--surface); | |
| color: inherit; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| display: grid; | |
| gap: 15px; | |
| min-height: 324px; | |
| align-content: start; | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), border-color 240ms cubic-bezier(0.16, 1, 0.3, 1), box-shadow 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .task-card:hover { transform: translateY(-3px); border-color: var(--green); } | |
| .task-card.active { | |
| border-color: rgba(164, 242, 127, 0.72); | |
| box-shadow: 0 20px 48px rgba(164, 242, 127, 0.08); | |
| } | |
| .task-card.hide { display: none; } | |
| .task-card-media { | |
| overflow: hidden; | |
| border: 1px solid rgba(164, 242, 127, 0.18); | |
| border-radius: 7px; | |
| background: #020502; | |
| } | |
| .task-card-media img { | |
| display: block; | |
| width: 100%; | |
| aspect-ratio: 16 / 8.5; | |
| object-fit: cover; | |
| transform: scale(1.01); | |
| } | |
| .task-top { display: flex; justify-content: space-between; gap: 14px; align-items: start; } | |
| .task-name { | |
| display: block; | |
| font-family: var(--font-ui); | |
| font-size: 21px; | |
| font-weight: 800; | |
| line-height: 1.08; | |
| letter-spacing: 0; | |
| text-wrap: balance; | |
| } | |
| .task-research-name { | |
| display: block; | |
| margin-top: 6px; | |
| color: #d7e5d1; | |
| font-size: 13px; | |
| line-height: 1.35; | |
| } | |
| .tag { | |
| font-size: 11px; | |
| border-radius: 999px; | |
| padding: 4px 8px; | |
| color: #d8ead2; | |
| background: rgba(164, 242, 127, 0.08); | |
| white-space: nowrap; | |
| } | |
| .tag.supervised { background: rgba(155, 223, 255, 0.12); color: #9bdfff; } | |
| .tag.forecast { background: rgba(164, 242, 127, 0.12); color: #a7f078; } | |
| .tag.retrieval { background: rgba(122, 229, 195, 0.12); color: #7ae5c3; } | |
| .tag.diagnostic { background: rgba(216, 244, 165, 0.12); color: #d8f4a5; } | |
| .task-card p { margin: 0; color: var(--muted); font-size: 13px; } | |
| .task-contract { | |
| display: grid; | |
| gap: 8px; | |
| color: #dce8d6; | |
| font-size: 12px; | |
| line-height: 1.4; | |
| } | |
| .task-contract span { | |
| display: grid; | |
| grid-template-columns: 58px minmax(0, 1fr); | |
| gap: 10px; | |
| align-items: baseline; | |
| border-top: 1px solid var(--soft-line); | |
| padding-top: 8px; | |
| } | |
| .task-contract strong { | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .metric-row { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 8px; | |
| font-size: 12px; | |
| } | |
| .metric-row span { | |
| display: block; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| padding: 9px; | |
| color: var(--muted); | |
| min-width: 0; | |
| } | |
| .metric-row strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 18px; | |
| line-height: 1.1; | |
| font-variant-numeric: tabular-nums; | |
| word-break: break-word; | |
| } | |
| .mini-bar { height: 7px; background: rgba(164, 242, 127, 0.14); border-radius: 999px; overflow: hidden; } | |
| .mini-bar span { display: block; height: 100%; width: var(--w); background: var(--c); } | |
| .artifact-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 18px; | |
| } | |
| .artifact-library { | |
| display: grid; | |
| gap: 24px; | |
| } | |
| .artifact-group { | |
| border: 1px solid var(--soft-line); | |
| border-radius: var(--radius); | |
| background: | |
| linear-gradient(180deg, rgba(164, 242, 127, 0.045), rgba(7, 18, 7, 0.62)), | |
| var(--panel); | |
| padding: 20px; | |
| } | |
| .artifact-group-head { | |
| display: grid; | |
| grid-template-columns: minmax(0, 0.75fr) minmax(280px, 0.95fr); | |
| gap: 24px; | |
| align-items: end; | |
| padding-bottom: 18px; | |
| margin-bottom: 18px; | |
| border-bottom: 1px solid var(--soft-line); | |
| } | |
| .artifact-group-head span { | |
| display: block; | |
| margin-bottom: 8px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-weight: 740; | |
| text-transform: uppercase; | |
| letter-spacing: 0.08em; | |
| } | |
| .artifact-group-head h3 { | |
| margin: 0; | |
| font-size: 24px; | |
| line-height: 1.06; | |
| } | |
| .artifact-group-head p { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.6; | |
| font-size: 14px; | |
| } | |
| .chart-grid { | |
| display: grid; | |
| grid-template-columns: minmax(0, 1fr); | |
| gap: 22px; | |
| align-items: start; | |
| } | |
| .direction-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin-bottom: 24px; | |
| } | |
| .direction-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| padding: 18px; | |
| min-height: 230px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| } | |
| .direction-card h3 { margin: 0; font-size: 16px; line-height: 1.25; } | |
| .direction-card p { margin: 0; color: var(--muted); font-size: 13px; line-height: 1.55; } | |
| .status-pill { | |
| width: fit-content; | |
| border-radius: 999px; | |
| padding: 4px 9px; | |
| background: rgba(164, 242, 127, 0.10); | |
| color: var(--green); | |
| font-size: 11px; | |
| font-weight: 740; | |
| } | |
| .direction-counts { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 8px; | |
| font-size: 12px; | |
| color: #bcc8b7; | |
| } | |
| .direction-counts strong { | |
| display: block; | |
| font-family: var(--font-mono); | |
| font-size: 18px; | |
| color: var(--ink); | |
| } | |
| .baseline-strip { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 14px; | |
| margin-top: 18px; | |
| } | |
| .extension-grid { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 16px; | |
| margin: 22px 0 24px; | |
| } | |
| .extension-card { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| padding: 18px; | |
| display: grid; | |
| gap: 12px; | |
| align-content: start; | |
| min-height: 282px; | |
| } | |
| .extension-card h3 { margin: 0; font-size: 15px; line-height: 1.3; } | |
| .extension-card p { margin: 0; color: var(--muted); font-size: 13px; line-height: 1.55; } | |
| .extension-metrics { | |
| display: grid; | |
| grid-template-columns: repeat(2, minmax(0, 1fr)); | |
| gap: 8px; | |
| font-size: 12px; | |
| color: var(--muted); | |
| } | |
| .extension-metrics strong { | |
| display: block; | |
| font-family: var(--font-mono); | |
| font-size: 18px; | |
| color: var(--ink); | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .task-player { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| display: grid; | |
| grid-template-columns: minmax(0, 1.05fr) minmax(360px, 0.95fr); | |
| gap: 24px; | |
| padding: clamp(18px, 2.4vw, 28px); | |
| background: | |
| linear-gradient(180deg, rgba(164, 242, 127, 0.07), rgba(7, 18, 7, 0.88)), | |
| var(--surface); | |
| box-shadow: 0 20px 58px rgba(0, 0, 0, 0.32); | |
| } | |
| .player-stage, | |
| .player-copy { | |
| min-width: 0; | |
| } | |
| .player-screen { | |
| position: relative; | |
| overflow: hidden; | |
| border: 1px solid rgba(164, 242, 127, 0.22); | |
| border-radius: var(--radius); | |
| background: #020502; | |
| aspect-ratio: 16 / 10; | |
| } | |
| .player-screen img { | |
| width: 100%; | |
| height: 100%; | |
| object-fit: cover; | |
| display: block; | |
| } | |
| .player-badge { | |
| position: absolute; | |
| left: 14px; | |
| bottom: 14px; | |
| max-width: calc(100% - 28px); | |
| border: 1px solid rgba(164, 242, 127, 0.42); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.78); | |
| color: #f4f8ef; | |
| padding: 10px 12px; | |
| backdrop-filter: blur(12px); | |
| } | |
| .player-badge strong { | |
| display: block; | |
| font-family: var(--font-ui); | |
| font-size: clamp(20px, 2.4vw, 34px); | |
| line-height: 1.08; | |
| text-wrap: balance; | |
| word-spacing: 0.06em; | |
| } | |
| .player-badge span { | |
| display: block; | |
| margin-top: 3px; | |
| color: var(--green); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| } | |
| .player-frame-chip { | |
| position: absolute; | |
| left: 14px; | |
| top: 14px; | |
| border: 1px solid rgba(164, 242, 127, 0.36); | |
| border-radius: 999px; | |
| background: rgba(2, 5, 2, 0.74); | |
| color: #dce8d6; | |
| padding: 7px 10px; | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| font-weight: 740; | |
| letter-spacing: 0.04em; | |
| text-transform: uppercase; | |
| backdrop-filter: blur(12px); | |
| } | |
| .player-frame-caption { | |
| margin: 12px 0 0; | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.48); | |
| color: #dce8d6; | |
| padding: 11px 12px; | |
| font-size: 13px; | |
| line-height: 1.5; | |
| } | |
| .player-controls { | |
| display: flex; | |
| align-items: center; | |
| gap: 9px; | |
| flex-wrap: wrap; | |
| margin-top: 14px; | |
| } | |
| .player-controls button { | |
| border: 1px solid var(--line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.62); | |
| color: #eaf5e5; | |
| min-height: 38px; | |
| padding: 0 12px; | |
| font: inherit; | |
| font-size: 13px; | |
| font-weight: 720; | |
| cursor: pointer; | |
| } | |
| .player-controls button.primary-control { | |
| background: var(--green); | |
| color: #020502; | |
| border-color: var(--green); | |
| } | |
| .player-counter { | |
| margin-left: auto; | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 12px; | |
| font-variant-numeric: tabular-nums; | |
| } | |
| .player-progress { | |
| height: 6px; | |
| margin-top: 12px; | |
| border-radius: 999px; | |
| background: rgba(164, 242, 127, 0.12); | |
| overflow: hidden; | |
| } | |
| .player-progress span { | |
| display: block; | |
| width: 0; | |
| height: 100%; | |
| border-radius: inherit; | |
| background: linear-gradient(90deg, var(--green), var(--cyan)); | |
| transition: width 260ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .task-scrubber { | |
| flex: 1 1 210px; | |
| min-width: 180px; | |
| accent-color: var(--green); | |
| cursor: pointer; | |
| } | |
| .storyboard-steps { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 8px; | |
| margin-top: 12px; | |
| } | |
| .story-button { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.52); | |
| color: #dce8d6; | |
| min-height: 48px; | |
| padding: 9px 8px; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| } | |
| .story-button strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .story-button span { | |
| display: block; | |
| margin-top: 4px; | |
| color: var(--muted); | |
| font-size: 12px; | |
| line-height: 1.25; | |
| } | |
| .story-button.active { | |
| border-color: rgba(164, 242, 127, 0.72); | |
| background: rgba(164, 242, 127, 0.12); | |
| } | |
| .modality-strip { | |
| display: grid; | |
| grid-template-columns: repeat(auto-fit, minmax(96px, 1fr)); | |
| gap: 9px; | |
| margin-top: 12px; | |
| } | |
| .modality-tile { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.48); | |
| overflow: hidden; | |
| min-width: 0; | |
| } | |
| .modality-tile img { | |
| width: 100%; | |
| aspect-ratio: 16 / 9; | |
| object-fit: cover; | |
| display: block; | |
| background: #020502; | |
| } | |
| .modality-tile span { | |
| display: block; | |
| padding: 7px 8px; | |
| color: #dce8d6; | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| line-height: 1.2; | |
| text-transform: uppercase; | |
| } | |
| .player-copy { | |
| display: grid; | |
| gap: 18px; | |
| align-content: start; | |
| } | |
| .player-kicker { | |
| display: flex; | |
| align-items: center; | |
| gap: 10px; | |
| flex-wrap: wrap; | |
| } | |
| .player-copy h3 { | |
| margin: 0; | |
| font-family: var(--font-ui); | |
| font-size: clamp(30px, 3.4vw, 48px); | |
| line-height: 1.02; | |
| text-wrap: balance; | |
| } | |
| .player-copy p { | |
| margin: 0; | |
| color: var(--muted); | |
| line-height: 1.62; | |
| } | |
| .player-case { | |
| color: #eaf5e5; | |
| font-size: 16px; | |
| } | |
| .flow-steps { | |
| display: grid; | |
| grid-template-columns: repeat(3, minmax(0, 1fr)); | |
| gap: 10px; | |
| } | |
| .flow-step, | |
| .module-list li { | |
| border: 1px solid var(--soft-line); | |
| background: rgba(164, 242, 127, 0.06); | |
| border-radius: 6px; | |
| padding: 10px; | |
| min-width: 0; | |
| } | |
| .flow-step { | |
| color: inherit; | |
| font: inherit; | |
| text-align: left; | |
| cursor: pointer; | |
| } | |
| .flow-step.active { | |
| border-color: rgba(164, 242, 127, 0.72); | |
| background: rgba(164, 242, 127, 0.12); | |
| } | |
| .flow-step strong, | |
| .module-list strong { | |
| display: block; | |
| margin-bottom: 5px; | |
| color: var(--ink); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| letter-spacing: 0.04em; | |
| } | |
| .flow-step em { | |
| display: block; | |
| color: #dce8d6; | |
| font-style: normal; | |
| line-height: 1.38; | |
| } | |
| .module-list { | |
| display: grid; | |
| gap: 8px; | |
| margin: 0; | |
| padding: 0; | |
| list-style: none; | |
| color: #dce8d6; | |
| font-size: 13px; | |
| line-height: 1.45; | |
| } | |
| .task-selector { | |
| display: grid; | |
| grid-template-columns: repeat(4, minmax(0, 1fr)); | |
| gap: 10px; | |
| margin-top: 18px; | |
| } | |
| .selector-button { | |
| border: 1px solid var(--soft-line); | |
| border-radius: 6px; | |
| background: rgba(2, 5, 2, 0.54); | |
| color: #dce8d6; | |
| font: inherit; | |
| min-height: 64px; | |
| padding: 10px; | |
| text-align: left; | |
| cursor: pointer; | |
| } | |
| .selector-button strong { | |
| display: block; | |
| color: var(--ink); | |
| font-family: var(--font-ui); | |
| font-size: 14px; | |
| line-height: 1.15; | |
| } | |
| .selector-button span { | |
| display: block; | |
| margin-top: 4px; | |
| color: var(--muted); | |
| font-family: var(--font-mono); | |
| font-size: 11px; | |
| text-transform: uppercase; | |
| } | |
| .selector-button.active { | |
| border-color: rgba(164, 242, 127, 0.72); | |
| background: rgba(164, 242, 127, 0.12); | |
| } | |
| .walk-flow { | |
| display: grid; | |
| grid-template-columns: 0.72fr 1.15fr 0.72fr; | |
| gap: 8px; | |
| align-items: stretch; | |
| font-size: 12px; | |
| } | |
| .walk-flow span { | |
| border: 1px solid var(--soft-line); | |
| background: rgba(164, 242, 127, 0.06); | |
| border-radius: 6px; | |
| padding: 9px; | |
| min-height: 58px; | |
| } | |
| .walk-flow strong { display: block; color: var(--ink); font-size: 11px; margin-bottom: 4px; text-transform: uppercase; letter-spacing: 0.04em; } | |
| .artifact { | |
| border: 1px solid var(--line); | |
| border-radius: var(--radius); | |
| background: var(--surface); | |
| padding: 18px; | |
| min-height: 164px; | |
| display: grid; | |
| align-content: start; | |
| transition: transform 240ms cubic-bezier(0.16, 1, 0.3, 1), border-color 240ms cubic-bezier(0.16, 1, 0.3, 1); | |
| } | |
| .artifact.primary-artifact { | |
| grid-column: 1 / -1; | |
| grid-template-columns: minmax(0, 1fr) auto; | |
| gap: 18px; | |
| align-items: end; | |
| min-height: 0; | |
| background: | |
| linear-gradient(120deg, rgba(164, 242, 127, 0.13), rgba(122, 229, 195, 0.05)), | |
| var(--surface); | |
| } | |
| .artifact:hover { transform: translateY(-3px); border-color: var(--green); } | |
| .artifact h3 { line-height: 1.18; } | |
| .artifact a { display: inline-block; margin-top: 14px; font-weight: 740; text-decoration: none; color: var(--blue); } | |
| .artifact a:hover { text-decoration: underline; text-underline-offset: 4px; } | |
| .repro-note { | |
| margin: 0 0 18px; | |
| color: var(--muted); | |
| font-size: 14px; | |
| line-height: 1.65; | |
| } | |
| .code-panel { | |
| background: #000; | |
| color: #dff7d4; | |
| border-radius: var(--radius); | |
| padding: 18px; | |
| overflow: auto; | |
| font-family: var(--font-mono); | |
| font-size: 13px; | |
| line-height: 1.65; | |
| border: 1px solid rgba(164, 242, 127, 0.24); | |
| } | |
| .code-panel button { | |
| float: right; | |
| margin-left: 16px; | |
| height: 30px; | |
| border: 1px solid rgba(164, 242, 127, 0.36); | |
| color: #020502; | |
| background: var(--green); | |
| border-radius: 5px; | |
| cursor: pointer; | |
| font-weight: 700; | |
| } | |
| footer { | |
| padding: 42px 0; | |
| color: var(--muted); | |
| font-size: 14px; | |
| } | |
| @media (max-width: 960px) { | |
| .hero-inner, .two-col { grid-template-columns: 1fr; } | |
| .hero-inner { min-height: 0; } | |
| .project-tabs { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| .section-tabs { padding-top: 10px; } | |
| .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .task-player { grid-template-columns: 1fr; } | |
| .task-selector { grid-template-columns: repeat(3, minmax(0, 1fr)); } | |
| .storyboard-steps { grid-template-columns: repeat(2, minmax(0, 1fr)); } | |
| .modality-atlas { grid-template-columns: 1fr; } | |
| .artifact-group-head { grid-template-columns: 1fr; align-items: start; } | |
| .chart-grid { grid-template-columns: 1fr; } | |
| .section-head { display: block; } | |
| .section-head p { margin-top: 14px; } | |
| .atlas-head { display: block; } | |
| .atlas-head a { display: inline-flex; margin-top: 14px; } | |
| } | |
| @media (max-width: 1120px) { | |
| .nav-links { display: none; } | |
| } | |
| @media (max-width: 640px) { | |
| .wrap { width: min(100% - 28px, var(--max)); } | |
| .project-tabs-shell { top: 64px; padding: 10px 0; } | |
| .project-tabs { | |
| display: flex; | |
| grid-template-columns: none; | |
| gap: 8px; | |
| overflow-x: auto; | |
| padding-bottom: 4px; | |
| scroll-snap-type: x proximity; | |
| scrollbar-width: thin; | |
| scrollbar-color: rgba(164, 242, 127, 0.42) rgba(7, 18, 7, 0.72); | |
| } | |
| .project-tab { | |
| flex: 0 0 min(42vw, 168px); | |
| min-height: 50px; | |
| padding: 10px 11px; | |
| scroll-snap-align: start; | |
| } | |
| .project-tab strong { font-size: 14px; } | |
| .project-tab span { | |
| font-size: 10.5px; | |
| line-height: 1.25; | |
| } | |
| .section-tabs { | |
| flex-wrap: nowrap; | |
| gap: 8px; | |
| overflow-x: auto; | |
| padding-top: 8px; | |
| padding-bottom: 4px; | |
| } | |
| .section-tab { | |
| flex: 0 0 auto; | |
| min-height: 34px; | |
| padding: 7px 10px; | |
| font-size: 12px; | |
| white-space: nowrap; | |
| } | |
| .content-tabs { | |
| margin-bottom: 14px; | |
| } | |
| .content-tab { | |
| min-width: min(76vw, 210px); | |
| min-height: 52px; | |
| } | |
| main > section { scroll-margin-top: 184px; } | |
| .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .atlas-rows { grid-template-columns: 1fr; } | |
| .artifact-group { padding: 16px; } | |
| .modality-atlas-panel { padding: 14px; } | |
| .atlas-card { padding: 12px; } | |
| .atlas-top { display: block; } | |
| .atlas-type { margin-top: 8px; text-align: left; } | |
| .atlas-row { grid-template-columns: 1fr; gap: 6px; } | |
| .atlas-card.wide img { | |
| aspect-ratio: 760 / 470; | |
| object-fit: cover; | |
| } | |
| .artifact.primary-artifact { grid-template-columns: 1fr; } | |
| .evidence-card:last-child { grid-column: auto; } | |
| .hero-inner, section { padding: 46px 0; } | |
| .signal { grid-template-columns: 1fr; } | |
| .signal strong { text-align: left; } | |
| .player-counter { width: 100%; margin-left: 0; } | |
| .task-scrubber { flex-basis: 100%; } | |
| .figure-pan { | |
| margin-inline: 0; | |
| padding-inline: 0; | |
| } | |
| .figure-pan .task-suite-image { | |
| width: 100%; | |
| min-width: 0; | |
| max-width: 100%; | |
| } | |
| } | |
| @media (prefers-reduced-motion: reduce) { | |
| html { scroll-behavior: auto; } | |
| *, *::before, *::after { | |
| transition-duration: 1ms ; | |
| animation-duration: 1ms ; | |
| animation-iteration-count: 1 ; | |
| } | |
| .motion-ready .reveal { opacity: 1; transform: none; } | |
| } | |
| </style> | |
| </head> | |
| <body> | |
| <a class="skip-link" href="#main">Skip to content</a> | |
| <nav class="site-nav"> | |
| <div class="wrap nav-inner"> | |
| <a class="brand" href="#top" aria-label="Ropedia Xperience-10M Task Suite home"> | |
| <img class="brand-logo" src="assets/brand/xperience10m-logo-favicon-64.png" alt="" aria-hidden="true" width="38" height="38"> | |
| <span>Ropedia Xperience-10M Task Suite</span> | |
| </a> | |
| <div class="nav-links" aria-label="Page navigation"> | |
| <a href="#overview">Overview</a> | |
| <a href="#dataset-card">Dataset</a> | |
| <a href="#suite">Tasks</a> | |
| <a href="#pipeline">Method</a> | |
| <a href="#models">Results</a> | |
| <a href="#directions">Directions</a> | |
| <a href="#walkthroughs">Walkthrough</a> | |
| <a href="#artifacts">Resources</a> | |
| <a class="nav-action" href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite">HF Space</a> | |
| <a class="nav-action" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite">GitHub</a> | |
| </div> | |
| </div> | |
| </nav> | |
| <header class="hero" id="top"> | |
| <div class="wrap hero-inner"> | |
| <div> | |
| <div class="eyebrow">public sample episode / multimodal task lab</div> | |
| <h1>Ropedia Xperience-10M Research Task Lab.</h1> | |
| <p class="hero-copy"> | |
| This project uses the public Xperience-10M sample from Ropedia to explore | |
| embodied-AI task design, multimodal feature construction, lightweight | |
| baselines, and future Omni-model fine-tuning. It starts from the sample | |
| episode available now, then keeps the same data contracts ready for | |
| held-out multi-episode training when more Xperience-10M data is staged. | |
| </p> | |
| <div class="hero-actions"> | |
| <a class="button primary" href="#suite">Inspect 12 tasks</a> | |
| <a class="button" href="https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/">Open HF app</a> | |
| <a class="button" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite">View repository</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+12+4</strong><span>core, neural, and extension probes</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:#a7f078"></span></div><strong>2,121</strong></div> | |
| <div class="signal"><code>camera+imu</code><div class="track"><span style="--w:1.5%;--c:#7ae5c3"></span></div><strong>126</strong></div> | |
| <div class="signal"><code>depth</code><div class="track"><span style="--w:11.7%;--c:#d8f4a5"></span></div><strong>980</strong></div> | |
| <div class="signal"><code>video</code><div class="track"><span style="--w:49.1%;--c:#9bdfff"></span></div><strong>4,116</strong></div> | |
| <div class="signal"><code>language</code><div class="track"><span style="--w:10.7%;--c:#f4f8ef"></span></div><strong>896</strong></div> | |
| <div class="signal"><code>static</code><div class="track"><span style="--w:1.7%;--c:#a5afa2"></span></div><strong>139</strong></div> | |
| </div> | |
| </div> | |
| </header> | |
| <main id="main" class="tabbed" data-active-tab="start"> | |
| <div class="project-tabs-shell" aria-label="Project section tabs"> | |
| <div class="wrap project-tabs" role="tablist" aria-label="Project sections"> | |
| <button type="button" class="project-tab active" id="tab-start" role="tab" data-tab-key="start" data-default-section="overview" aria-selected="true" aria-pressed="true" aria-controls="overview reading-path"> | |
| <strong>Start</strong> | |
| <span>project overview and roadmap</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card suite walkthroughs tasks" tabindex="-1"> | |
| <strong>Data & Tasks</strong> | |
| <span>dataset sample and task suite</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-method" role="tab" data-tab-key="method" data-default-section="pipeline" aria-selected="false" aria-pressed="false" aria-controls="protocol pipeline architectures features" tabindex="-1"> | |
| <strong>Method</strong> | |
| <span>pipeline and model design</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-results" role="tab" data-tab-key="results" data-default-section="takeaways" aria-selected="false" aria-pressed="false" aria-controls="takeaways models neural directions extensions diagnostics" tabindex="-1"> | |
| <strong>Results</strong> | |
| <span>baselines and research tracks</span> | |
| </button> | |
| <button type="button" class="project-tab" id="tab-resources" role="tab" data-tab-key="resources" data-default-section="artifacts" aria-selected="false" aria-pressed="false" aria-controls="evidence artifacts omni-relay run" tabindex="-1"> | |
| <strong>Resources</strong> | |
| <span>research artifacts and scale-up</span> | |
| </button> | |
| </div> | |
| <div class="wrap section-tabs" id="sectionTabs" role="tablist" aria-label="Sections inside the selected project tab"></div> | |
| </div> | |
| <section id="overview" data-project-tab="start" role="tabpanel" aria-labelledby="tab-start" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Project overview and contributions.</h2> | |
| <p>The page is organized like a compact research project: motivation and scope, dataset sample, task suite, method, baselines, research directions, interactive walkthroughs, and resources for continuing the work.</p> | |
| </div> | |
| <div class="snapshot-grid"> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Multimodal episode pipeline</h3> | |
| <p>One Xperience-10M public sample episode is converted into aligned windows and a documented feature contract.</p> | |
| <div class="snapshot-meta"> | |
| <span>frames <strong>5,821</strong></span> | |
| <span>windows <strong>1,161</strong></span> | |
| <span>features <strong>8,378</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Task suite and baseline heads</h3> | |
| <p>Every core task has a minimal baseline and a compact PyTorch MLP head over the same windows, splits, and labels.</p> | |
| <div class="snapshot-meta"> | |
| <span>core tasks <strong>12</strong></span> | |
| <span>neural heads <strong>12</strong></span> | |
| <span>extension probes <strong>4</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Dataset source alignment</h3> | |
| <p>The public description is aligned to the official gated Xperience-10M dataset card, including modalities, scale, access, and current project coverage.</p> | |
| <div class="snapshot-meta"> | |
| <span>full dataset <strong>gated</strong></span> | |
| <span>sample scope <strong>1 episode</strong></span> | |
| <span>raw data mirrored <strong>no</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Public research artifacts</h3> | |
| <p>Metrics, figures, walkthrough data, baseline weights, and project metadata are packaged across GitHub, GitHub Pages, and Hugging Face.</p> | |
| <div class="snapshot-meta"> | |
| <span>site integrity <strong>pass</strong></span> | |
| <span>mirror parity <strong>pass</strong></span> | |
| <span>live status <strong>pass</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card gated"> | |
| <span class="status-pill">data-gated</span> | |
| <h3>Omni-model scale-up path</h3> | |
| <p>The 32-episode LoRA path is prepared; full training results require gated data access, held-out splits, training, and evaluation.</p> | |
| <div class="snapshot-meta"> | |
| <span>current stage <strong>setup checked</strong></span> | |
| <span>target gate <strong>32 episodes</strong></span> | |
| <span>held-out eval <strong>pending</strong></span> | |
| </div> | |
| </article> | |
| <article class="snapshot-card gated"> | |
| <span class="status-pill">not redistributed</span> | |
| <h3>Data governance</h3> | |
| <p>Raw MP4/HDF5/RRD files, private gated Xperience-10M data, and full Qwen weights are excluded from the public repo and HF mirrors.</p> | |
| <div class="snapshot-meta"> | |
| <span>raw Xperience-10M <strong>excluded</strong></span> | |
| <span>full Qwen weights <strong>excluded</strong></span> | |
| <span>derived artifacts <strong>included</strong></span> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="snapshot-actions"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/PROJECT_STATUS.md">Project status</a> | |
| <a href="data/project_status.json">Project status JSON</a> | |
| <a href="data/project_packet.json">Project packet JSON</a> | |
| <a href="data/live_publication_status.json">live_publication_status.json</a> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="protocol" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Evaluation protocol is explicit.</h2> | |
| <p>The protocol is generated from committed metric artifacts so readers can see the exact data unit, split, task targets, leakage controls, and current limitations before comparing scores.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Data unit</h3><p>One 20-frame aligned window from the public sample episode, stride 5 frames, 1,161 windows total, represented by the current 8,378-d feature vector.</p></div><a href="data/evaluation_protocol.json">protocol JSON</a></article> | |
| <article class="artifact"><h3>Split policy</h3><p>Single-episode chronological 70/30 train/test split. This avoids random future-window mixing; cross-episode generalization is measured in the later multi-episode pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVALUATION_PROTOCOL.md">protocol doc</a></article> | |
| <article class="artifact"><h3>Metric contract</h3><p>All 12 tasks list input, target, primary metric, minimal baseline score, and neural MLP score from committed result files.</p><a href="data/summary_metrics.json">summary metrics</a></article> | |
| <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target feature blocks, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article> | |
| <article class="artifact"><h3>Current limits</h3><p>This public-sample run does not evaluate cross-episode generalization, audio-visual learning, pixel-depth reconstruction, neural rendering, or full 32-episode Qwen3-Omni training.</p><a href="data/scope_claims_audit.json">status check</a></article> | |
| <article class="artifact"><h3>Scale-up gate</h3><p>The Omni pilot requires at least 32 valid episodes, held-out episode splits, no train/test episode leakage, training metadata, predictions, metrics, and a run report.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">data gate</a></article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="evidence" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Research progress and next milestones.</h2> | |
| <p>The project shows the completed public-sample task suite, then lays out the data requirements for the Qwen3-Omni scale-up path.</p> | |
| </div> | |
| <div class="evidence-grid"> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Aligned Xperience-10M sample windows</h3> | |
| <p>5,821 frames become 1,161 synchronized 20-frame windows with an explicit 8,378-d feature contract.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/summary_report.json">summary_report.json</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature_manifest.json</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>12 minimal heads + 12 neural MLP heads</h3> | |
| <p>Every task has a minimal interpretable head and a matching neural MLP run over the same windows, splits, and task contract.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite">task artifacts</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural_mlp/</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Four research directions are mapped by evidence type</h3> | |
| <p>The Ropedia directions are labeled as direct, proxy, or diagnostic coverage, plus one coded extension probe per direction.</p> | |
| <div class="evidence-links"> | |
| <a href="data/research_directions.json">directions JSON</a> | |
| <a href="data/research_direction_extensions.json">extensions JSON</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">data-gated</span> | |
| <h3>Qwen3-Omni pilot setup</h3> | |
| <p>The current Qwen3-Omni artifacts use one episode and 128 train windows. The 32-episode evaluation is still pending.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">evidence contract</a> | |
| <a href="data/evidence_contract.json">machine JSON</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Scale-up status is machine-checked</h3> | |
| <p>The status check confirms historical <code>32ep</code> run/path strings stay in setup-file provenance and are not presented as completed 32-episode results.</p> | |
| <div class="evidence-links"> | |
| <a href="data/scope_claims_audit.json">status JSON</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/validate_scope_claims.py">validator script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Prepared mirrors are byte-checked</h3> | |
| <p>The parity report compares critical JSON, figure, and validator files across the repo, HF Space bundle, artifact dataset bundle, and model bundle before upload.</p> | |
| <div class="evidence-links"> | |
| <a href="data/mirror_parity.json">mirror parity</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/validate_mirror_parity.py">validator script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Figures are indexed as evidence</h3> | |
| <p>The figure index records public visual assets, dimensions, SHA-256 hashes, source scripts, and the role each figure plays in the project narrative.</p> | |
| <div class="evidence-links"> | |
| <a href="data/figure_index.json">figure_index.json</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FIGURE_INDEX.md">FIGURE_INDEX.md</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_figure_index.py">builder script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Brand assets are packaged consistently</h3> | |
| <p>The generated logo system is packaged into the website header, favicon, README/HF cards, Open Graph preview, and brand-asset manifest.</p> | |
| <div class="evidence-links"> | |
| <a href="data/brand_assets.json">brand_assets.json</a> | |
| <a href="assets/brand/xperience10m-logo-social-card.png">logo card</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_brand_assets.py">builder script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Publication bundles are checked before release</h3> | |
| <p>The validator checks required assets, raw-data exclusion, Python cache exclusion, heavy archive exclusion, accidental HF token strings, and public-card figure freshness across GitHub and the HF bundles.</p> | |
| <div class="evidence-links"> | |
| <a href="data/publication_audit.json">package check</a> | |
| <a href="data/artifact_index.json">artifact index</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/validate_publication_package.py">validator script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Website integrity is checked</h3> | |
| <p>The site validator checks local links, anchors, JSON bundles, and referenced image dimensions before publishing.</p> | |
| <div class="evidence-links"> | |
| <a href="data/website_integrity.json">website_integrity.json</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/validate_website_integrity.py">validator script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Release gates are explicit</h3> | |
| <p>The quality-gate manifest collects the automated validators and the live post-publish checks required before the release is presented as current.</p> | |
| <div class="evidence-links"> | |
| <a href="data/quality_gates.json">quality_gates.json</a> | |
| <a href="data/live_publication_status.json">live publication</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/QUALITY_GATES.md">QUALITY_GATES.md</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_quality_gates.py">builder script</a> | |
| </div> | |
| </article> | |
| <article class="evidence-card"> | |
| <span class="status-pill">verified</span> | |
| <h3>Official dataset card is aligned</h3> | |
| <p>The source-alignment note mirrors the public Hugging Face dataset-card facts, sample-card facts, and API metadata: gated access, sample license/tooling, modality coverage, episode layout, intended uses, and current project coverage.</p> | |
| <div class="evidence-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">alignment note</a> | |
| <a href="data/xperience10m_dataset_card_alignment.json">alignment JSON</a> | |
| <a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official dataset</a> | |
| </div> | |
| </article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="reading-path" data-project-tab="start" role="tabpanel" aria-labelledby="tab-start" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Research reading path.</h2> | |
| <p>A newcomer should be able to move from the dataset sample to the task design, model baselines, current limitations, and scale-up plan without reading every file first.</p> | |
| </div> | |
| <div class="reading-grid"> | |
| <article class="reading-card"> | |
| <span class="step-index">01</span> | |
| <h3>Understand the current scope</h3> | |
| <p>Start with the project status, evidence contract, artifact index, scale-up status check, publication package check, and website integrity report. They separate implemented single-episode work from the prepared Qwen3-Omni scale-up.</p> | |
| <div class="reading-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">contract</a> | |
| <a href="data/evidence_contract.json">JSON</a> | |
| <a href="data/xperience10m_dataset_card_alignment.json">dataset card</a> | |
| <a href="data/artifact_index.json">index</a> | |
| <a href="data/figure_index.json">figures</a> | |
| <a href="data/brand_assets.json">brand</a> | |
| <a href="data/mirror_parity.json">mirrors</a> | |
| <a href="data/project_status.json">status</a> | |
| <a href="data/project_packet.json">packet</a> | |
| <a href="data/scope_claims_audit.json">scope</a> | |
| <a href="data/publication_audit.json">package</a> | |
| <a href="data/website_integrity.json">site check</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">02</span> | |
| <h3>Inspect one model input</h3> | |
| <p>Use the window table and feature manifest to see the exact aligned sample unit, feature blocks, dimensions, and omitted audio feature status.</p> | |
| <div class="reading-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">windows</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">features</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">03</span> | |
| <h3>Compare minimal vs neural heads</h3> | |
| <p>Every task has a small interpretable baseline and a matching neural MLP head over the same feature contract and chronological split.</p> | |
| <div class="reading-links"> | |
| <a href="data/summary_metrics.json">summary metrics</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural heads</a> | |
| </div> | |
| </article> | |
| <article class="reading-card"> | |
| <span class="step-index">04</span> | |
| <h3>Check the scale-up gate</h3> | |
| <p>The multi-episode Qwen3-Omni path is prepared. The 32-episode result will be added after the data gate and held-out evaluation pass.</p> | |
| <div class="reading-links"> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">blocker</a> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">access status</a> | |
| <a href="data/project_packet.json">project packet</a> | |
| </div> | |
| </article> | |
| </div> | |
| <div class="boundary-strip"> | |
| <div class="boundary-item"><strong>Verified now</strong><span>One public episode, 5,821 frames, 1,161 windows, 8,378 current features, 12 minimal heads, 12 neural heads, and 4 direction-extension probes.</span></div> | |
| <div class="boundary-item"><strong>Pending scale</strong><span>A 32-episode held-out Qwen3-Omni LoRA pilot is gated on Xperience-10M access and must pass manifest, training, and evaluation checks.</span></div> | |
| <div class="boundary-item"><strong>Not redistributed</strong><span>Raw videos, raw annotations, full Qwen weights, and private gated Xperience-10M data are not included in the public repo or HF bundles.</span></div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="dataset-card" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Aligned with the official dataset card.</h2> | |
| <p>The official Xperience-10M card describes a gated, large-scale 4D egocentric multimodal dataset. This project records that full upstream scope while focusing the implemented artifacts on one public sample episode.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Official scale</h3><p>About 10M experience units and 10,000 hours, with RGB video, audio, depth, camera pose/SLAM, hand/body mocap, IMU, captions, metadata, and calibration.</p></div><a href="data/xperience10m_dataset_card_alignment.json">alignment JSON</a></article> | |
| <article class="artifact"><h3>HF file-size display</h3><p>The live Hugging Face page/API currently shows 31.9 TB hosted. This is recorded separately from the card's about-1PB full-scale storage statement.</p><a href="data/xperience10m_dataset_card_alignment.json">source JSON</a></article> | |
| <article class="artifact"><h3>HF access path</h3><p>The source dataset is manually gated for approved non-commercial use, with an external agreement step noted by the public HF metadata.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">official HF dataset</a></article> | |
| <article class="artifact"><h3>API listing snapshot</h3><p>HF API metadata observed 803 session folders and 12,103 episode folders with <code>annotation.hdf5</code>. This snapshot supports planning; it is not a local data inventory.</p><a href="data/xperience10m_dataset_card_alignment.json">metadata JSON</a></article> | |
| <article class="artifact"><h3>Public sample card</h3><p>The sample repo lists <code>cc-by-nc-4.0</code>, HOMIE Toolkit for videos/annotations, and Rerun 0.29.0 for <code>.rrd</code> visualization.</p><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">sample dataset</a></article> | |
| <article class="artifact"><h3>Source alignment</h3><p>The source-alignment validator checks full-dataset facts, public sample-card facts, API-listing caveats, and current-project markers across the repo, website, and HF cards.</p><a href="data/source_alignment_audit.json">alignment report</a></article> | |
| <article class="artifact"><h3>Episode layout</h3><p>Expected folders contain six MP4 streams and <code>annotation.hdf5</code>; <code>visualization.rrd</code> is treated as a viewer artifact and excluded from training downloads.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">alignment note</a></article> | |
| <article class="artifact"><h3>Current project subset</h3><p>One public sample episode, 5,821 frames, 1,161 windows, 8,378 current features, audio documented but not yet featurized, and no raw-data redistribution.</p><a href="data/modality_atlas.json">modality atlas</a></article> | |
| <article class="artifact"><h3>Covered now</h3><p>Action/subtask labels, next-action prediction, temporal diagnostics, hand trajectory, contact, object relevance, caption grounding, retrieval, reconstruction, and misalignment.</p><a href="data/summary_metrics.json">summary metrics</a></article> | |
| <article class="artifact"><h3>Responsible use</h3><p>The official card notes limited diversity and showcase/production quality. This project excludes identity, surveillance, biometric, sensitive-attribute, and safety-critical uses.</p><a href="data/xperience10m_dataset_card_alignment.json">use notes</a></article> | |
| <article class="artifact"><h3>Later milestones</h3><p>Full audio-visual learning, caption generation, depth-pixel prediction, SLAM estimation, neural rendering, policy learning, cross-episode generalization, and 32-episode Qwen3-Omni evaluation.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">data gate</a></article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="suite" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Ropedia Xperience-10M 12-task suite, first.</h2> | |
| <p>Start with the full 12-task map, then inspect the large native modality atlas below it. Audio is present in the sample MP4 stream, but the current 8,378-d baseline manifest does not featurize it.</p> | |
| </div> | |
| <div class="figure-pan" id="task-suite-map"> | |
| <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v12-modality-xl" alt="Infographic showing all 12 Ropedia Xperience-10M tasks with enlarged full-width modality cards"> | |
| </div> | |
| <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title"> | |
| <div class="atlas-head"> | |
| <div> | |
| <h3 id="modality-atlas-title">Readable modality atlas.</h3> | |
| <p>Each Xperience-10M stream gets a large thumbnail, a plain sample-content line, and the exact current-baseline use. These are small derived images only; no raw MP4, HDF5, or RRD data is redistributed.</p> | |
| </div> | |
| <a href="data/modality_atlas.json">modality_atlas.json</a> | |
| </div> | |
| <div class="modality-atlas"> | |
| <article class="atlas-card"> | |
| <div class="atlas-top"><div><span class="atlas-index">01</span><h4>Video</h4></div><span class="atlas-type">visual stream</span></div> | |
| <img src="assets/modalities/video.jpg" alt="Public sample fisheye and stereo camera thumbnails" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>6 synchronized camera MP4 streams</p></div><div class="atlas-row"><span>current baseline use</span><p>RGB/fisheye/stereo frame statistics</p></div></div> | |
| </article> | |
| <article class="atlas-card audio-card"> | |
| <div class="atlas-top"><div><span class="atlas-index">02</span><h4>Audio</h4></div><span class="atlas-type">acoustic stream</span></div> | |
| <img src="assets/modalities/audio.png" alt="AAC waveform thumbnail from the public sample MP4 stream" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>AAC stream embedded in MP4</p></div><div class="atlas-row"><span>current baseline use</span><p>Documented, not featurized in the 8,378-d vector</p></div></div> | |
| </article> | |
| <article class="atlas-card"> | |
| <div class="atlas-top"><div><span class="atlas-index">03</span><h4>Depth</h4></div><span class="atlas-type">geometry map</span></div> | |
| <img src="assets/modalities/depth.jpg" alt="Public sample depth and confidence thumbnails" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>Depth map + confidence channel</p></div><div class="atlas-row"><span>current baseline use</span><p>Spatial geometry feature block</p></div></div> | |
| </article> | |
| <article class="atlas-card"> | |
| <div class="atlas-top"><div><span class="atlas-index">04</span><h4>Pose / SLAM</h4></div><span class="atlas-type">camera pose</span></div> | |
| <img src="assets/modalities/pose_slam.png" alt="Public sample camera trajectory and sparse SLAM map thumbnail" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>Trajectory + sparse SLAM map</p></div><div class="atlas-row"><span>current baseline use</span><p>Position + orientation features</p></div></div> | |
| </article> | |
| <article class="atlas-card"> | |
| <div class="atlas-top"><div><span class="atlas-index">05</span><h4>Motion Capture</h4></div><span class="atlas-type">human motion</span></div> | |
| <img src="assets/modalities/motion_capture.png" alt="Public sample body and hand motion capture thumbnail" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>Body + hand joint tracks</p></div><div class="atlas-row"><span>current baseline use</span><p>3D mocap feature statistics</p></div></div> | |
| </article> | |
| <article class="atlas-card"> | |
| <div class="atlas-top"><div><span class="atlas-index">06</span><h4>Inertial</h4></div><span class="atlas-type">wearable sensor</span></div> | |
| <img src="assets/modalities/inertial.png" alt="Public sample accelerometer and gyroscope time-series thumbnail" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>Accelerometer + gyroscope</p></div><div class="atlas-row"><span>current baseline use</span><p>Wearable motion statistics</p></div></div> | |
| </article> | |
| <article class="atlas-card wide"> | |
| <div class="atlas-top"><div><span class="atlas-index">07</span><h4>Language</h4></div><span class="atlas-type">semantic annotation</span></div> | |
| <img src="assets/modalities/language.png" alt="Public sample object tags and action caption thumbnail" loading="eager" decoding="async"> | |
| <div class="atlas-rows"><div class="atlas-row"><span>sample contains</span><p>Object tags + action captions</p></div><div class="atlas-row"><span>current baseline use</span><p>Task labels + semantic targets</p></div></div> | |
| </article> | |
| </div> | |
| <p class="atlas-note">The atlas redistributes only small derived thumbnails and metadata. Raw MP4, HDF5, and RRD files remain excluded from this repo and the Hugging Face mirrors.</p> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="pipeline" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <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=xperience10m-nn" alt="Verified Xperience-10M multimodal pipeline diagram"> | |
| <div class="callout-row"> | |
| <div class="callout"> | |
| <h3>What this project proves</h3> | |
| <p>It demonstrates the full development loop: reading Xperience-10M sample data, aligning modalities, converting them into model-ready windows, defining meaningful tasks, producing metrics, and packaging artifacts for continued research.</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="takeaways" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>What the current results actually say.</h2> | |
| <p>A generated takeaways layer reads the committed metrics and separates useful research signals from claims that still require held-out episodes.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"> | |
| <div> | |
| <h3>One episode becomes a benchmark contract</h3> | |
| <p>The public sample is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,378-dimensional feature contract.</p> | |
| </div> | |
| <a href="data/research_takeaways.json">research_takeaways.json</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Chronological split exposes class shift</h3> | |
| <p>All-feature action reaches 0.9791 macro-F1 on its local split, while the 12-task chronological action head is 0.0500 macro-F1 with four unseen later action labels.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/RESEARCH_TAKEAWAYS.md">takeaways</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Neural heads help dynamics</h3> | |
| <p>Hand MPJPE improves from 0.8223 to 0.1116; temporal-order F1 rises from 0.5487 to 0.8718; misalignment F1 rises from 0.4866 to 0.7335.</p> | |
| <a href="data/research_takeaways.json">metrics</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Retrieval and reconstruction remain open</h3> | |
| <p>Ridge/cosine retrieval remains stronger than the neural projection here, and cross-modal feature reconstruction still has negative R2.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">retrieval metrics</a> | |
| </article> | |
| <article class="artifact"> | |
| <h3>Scale means held-out episodes</h3> | |
| <p>The next credible model-quality unit is a 32-episode held-out pilot across 32 sessions, not more adjacent windows from one sample.</p> | |
| <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">scale-up status</a> | |
| </article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="models" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <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 inspect. The neural run keeps the same features and splits, then swaps in PyTorch MLP heads.</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="neural" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Neural MLP heads, same task contracts.</h2> | |
| <p>The neural baseline uses small PyTorch MLP classifiers/regressors on the same 8,378-d window features, chronological splits, and leakage filters. This isolates the value of a nonlinear head before moving to heavier Qwen/Omni experiments.</p> | |
| </div> | |
| <div class="models"> | |
| <article class="model"><h3>Neural hand forecast</h3><span class="score">0.1116</span><span class="meta">MPJPE, down from 0.8223 minimal</span></article> | |
| <article class="model"><h3>Neural temporal order</h3><span class="score">0.8718</span><span class="meta">F1, adjacent-window diagnostic</span></article> | |
| <article class="model"><h3>Neural misalignment</h3><span class="score">0.7335</span><span class="meta">F1, shifted motion/visual pairs</span></article> | |
| <article class="model"><h3>Neural cross-modal retrieval</h3><span class="score">0.1530</span><span class="meta">MRR; ridge remains stronger here</span></article> | |
| </div> | |
| <div class="chart-grid"> | |
| <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP episode task score chart"> | |
| <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural MLP episode task score chart"> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="directions" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>The 12 tasks organized into four research directions.</h2> | |
| <p>Each task is mapped as direct, proxy, or diagnostic evidence for the Ropedia research tracks. The mapping uses two current baselines: minimal interpretable heads and neural MLP heads over the same feature contract.</p> | |
| </div> | |
| <div class="direction-grid"> | |
| <article class="direction-card"> | |
| <span class="status-pill">partially implemented</span> | |
| <h3>A. Human Modeling & Motion Understanding</h3> | |
| <p>Direct evidence comes from hand trajectory forecasting and contact prediction; action and object relevance are supporting proxies.</p> | |
| <div class="direction-counts"><span><strong>2</strong>direct</span><span><strong>2</strong>proxy</span><span><strong>0</strong>diagnostic</span></div> | |
| </article> | |
| <article class="direction-card"> | |
| <span class="status-pill">proxy tasks only</span> | |
| <h3>B. 3D/4D Reconstruction & Neural Rendering</h3> | |
| <p>Cross-modal retrieval, modality reconstruction, and misalignment detection check reconstruction prerequisites, not full geometry.</p> | |
| <div class="direction-counts"><span><strong>0</strong>direct</span><span><strong>2</strong>proxy</span><span><strong>1</strong>diagnostic</span></div> | |
| </article> | |
| <article class="direction-card"> | |
| <span class="status-pill">strongest implemented</span> | |
| <h3>C. Egocentric Vision & Interaction</h3> | |
| <p>Action, subtask, transition, next-action, object, caption, order, and alignment tasks directly stress egocentric understanding.</p> | |
| <div class="direction-counts"><span><strong>6</strong>direct</span><span><strong>2</strong>proxy</span><span><strong>3</strong>diagnostic</span></div> | |
| </article> | |
| <article class="direction-card"> | |
| <span class="status-pill">early proxy tasks</span> | |
| <h3>D. Scene Reconstruction & World Modeling</h3> | |
| <p>Current probes cover task state, object relevance, retrieval, reconstruction, temporal order, and alignment but no persistent map yet.</p> | |
| <div class="direction-counts"><span><strong>0</strong>direct</span><span><strong>6</strong>proxy</span><span><strong>3</strong>diagnostic</span></div> | |
| </article> | |
| </div> | |
| <img class="chart" src="assets/charts/research_direction_coverage.svg" alt="Coverage of the 12 Xperience-10M tasks across four research directions"> | |
| <div class="baseline-strip"> | |
| <div class="callout"> | |
| <h3>Baseline 1: minimal heads</h3> | |
| <p>Softmax, logistic, ridge, and retrieval heads keep every input/output contract readable. They are the first sanity check for whether a task is well-posed.</p> | |
| </div> | |
| <div class="callout"> | |
| <h3>Baseline 2: neural MLP heads</h3> | |
| <p>Small PyTorch MLP classifiers/regressors reuse the same features and splits. They test nonlinear gains before heavier Omni fine-tuning.</p> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="extensions" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Four extra probes make the directions actionable.</h2> | |
| <p>These are new data-backed extension tasks computed from the same single-episode feature tensor. They add one concrete input, process, output, and metric for each research direction, while keeping the single-episode limitation explicit.</p> | |
| </div> | |
| <img class="chart" src="assets/charts/research_direction_extension_tasks.svg?v=xperience10m-ext" alt="Four Xperience-10M research-direction extension probes with minimal and neural metrics"> | |
| <div class="extension-grid"> | |
| <article class="extension-card"> | |
| <span class="status-pill">A / motion</span> | |
| <h3>Body and Hand Motion Intensity</h3> | |
| <p><strong>Case:</strong> classify fast reach/pour windows as high motion and steady holding windows as low motion.</p> | |
| <p><strong>Input:</strong> non-mocap video, depth, pose, IMU, SLAM, calibration, and language features.</p> | |
| <p><strong>Output:</strong> high_motion or low_motion.</p> | |
| <div class="extension-metrics"><span><strong>0.7827</strong>minimal macro-F1</span><span><strong>0.7986</strong>neural macro-F1</span></div> | |
| </article> | |
| <article class="extension-card"> | |
| <span class="status-pill">B / views</span> | |
| <h3>Multi-View Consistency Retrieval</h3> | |
| <p><strong>Case:</strong> retrieve the synchronized stereo-left window from a fisheye-camera query.</p> | |
| <p><strong>Input:</strong> fisheye_cam0 video features against stereo_left candidate features.</p> | |
| <p><strong>Output:</strong> ranked synchronized view candidates.</p> | |
| <div class="extension-metrics"><span><strong>0.5534</strong>minimal MRR</span><span><strong>0.3469</strong>neural MRR</span></div> | |
| </article> | |
| <article class="extension-card"> | |
| <span class="status-pill">C / phase</span> | |
| <h3>Action Phase Progress Estimation</h3> | |
| <p><strong>Case:</strong> estimate whether a Pour coffee window is near the start, middle, or end of its action segment.</p> | |
| <p><strong>Input:</strong> non-caption multimodal features.</p> | |
| <p><strong>Output:</strong> 0-to-1 progress inside the current action.</p> | |
| <div class="extension-metrics"><span><strong>0.3416</strong>minimal MAE</span><span><strong>0.3038</strong>neural MAE</span></div> | |
| </article> | |
| <article class="extension-card"> | |
| <span class="status-pill">D / world</span> | |
| <h3>Short-Horizon Ego-Motion Forecasting</h3> | |
| <p><strong>Case:</strong> predict how the camera translation changes over the next 20 frames.</p> | |
| <p><strong>Input:</strong> current sensors excluding camera translation and captions.</p> | |
| <p><strong>Output:</strong> future camera-translation delta vector.</p> | |
| <div class="extension-metrics"><span><strong>0.1989</strong>minimal MAE</span><span><strong>0.0989</strong>neural MAE</span></div> | |
| </article> | |
| </div> | |
| <div class="callout-row"> | |
| <div class="callout"> | |
| <h3>What changed</h3> | |
| <p>The four research directions now have coded extension probes, prediction/rank CSVs, JSON metrics, a Markdown summary, and a website chart generated from real sample-window features.</p> | |
| </div> | |
| <div class="callout"> | |
| <h3>What still needs scale</h3> | |
| <p>A full research result still needs many Xperience-10M episodes, held-out episode splits, stronger encoders, and direction-specific models such as body priors, renderers, or persistent scene graphs.</p> | |
| </div> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="architectures" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <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=xperience10m-nn" alt="Verified minimal and neural architecture diagram for all 12 Ropedia Xperience-10M tasks"> | |
| </div> | |
| </section> | |
| <section id="walkthroughs" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Interactive task walkthrough.</h2> | |
| <p>Each task uses a common research name and a concrete case study, then opens into the input, middle modules, output, modality evidence, metric, and current limitation.</p> | |
| </div> | |
| <div class="task-player" id="taskPlayer"> | |
| <div class="player-stage"> | |
| <div class="player-screen"> | |
| <img id="playerPoster" src="assets/modalities/video.jpg" alt="Representative sample modality for the selected task"> | |
| <div class="player-frame-chip" id="playerFrameChip">Step 1 / 4 · Input</div> | |
| <div class="player-badge"> | |
| <strong id="playerBadgeTitle">Action Recognition</strong> | |
| <span id="playerBadgeMeta">Egocentric Action Recognition</span> | |
| </div> | |
| </div> | |
| <p class="player-frame-caption" id="playerFrameCaption">Input: inspect the 20-frame multimodal window before choosing the target.</p> | |
| <div class="modality-strip" id="playerModalities" aria-label="Selected task modality evidence"></div> | |
| <div class="player-controls"> | |
| <button type="button" id="playerPrev">Previous</button> | |
| <button type="button" class="primary-control" id="playerPlay">Play</button> | |
| <button type="button" id="playerNext">Next</button> | |
| <input class="task-scrubber" id="playerScrub" type="range" min="0" max="11" value="0" step="1" aria-label="Scrub through task cards"> | |
| <span class="player-counter" id="playerCounter">01 / 12</span> | |
| </div> | |
| <div class="storyboard-steps" id="playerStoryboard" aria-label="Interactive walkthrough chapters"> | |
| <button type="button" class="story-button active" data-stage="0" aria-pressed="true"><strong>Input</strong><span>What enters the model</span></button> | |
| <button type="button" class="story-button" data-stage="1" aria-pressed="false"><strong>Process</strong><span>How the target is built</span></button> | |
| <button type="button" class="story-button" data-stage="2" aria-pressed="false"><strong>Output</strong><span>What is predicted</span></button> | |
| <button type="button" class="story-button" data-stage="3" aria-pressed="false"><strong>Evaluate</strong><span>Metric and limitation</span></button> | |
| </div> | |
| <div class="player-progress" aria-hidden="true"><span id="playerProgress"></span></div> | |
| </div> | |
| <article class="player-copy" aria-live="polite"> | |
| <div class="player-kicker"> | |
| <span class="tag supervised" id="playerFamily">supervised</span> | |
| <span class="status-pill" id="playerArchitecture">multiclass classifier</span> | |
| </div> | |
| <h3 id="playerTitle">Action Recognition</h3> | |
| <p class="player-case" id="playerCase">In the coffee-making sample, a pouring window maps to the current action label.</p> | |
| <div class="flow-steps"> | |
| <button type="button" class="flow-step active" data-stage="0" aria-pressed="true"><strong>Input</strong><em id="playerInput">20-frame multimodal window</em></button> | |
| <button type="button" class="flow-step" data-stage="1" aria-pressed="false"><strong>Process</strong><em id="playerProcess">window features -> classifier</em></button> | |
| <button type="button" class="flow-step" data-stage="2" aria-pressed="false"><strong>Output</strong><em id="playerOutput">current action class</em></button> | |
| </div> | |
| <ul class="module-list" id="playerModules"></ul> | |
| <p id="playerMetric">Metric: macro-F1. Minimal 0.0500; neural MLP 0.0263.</p> | |
| <p id="playerLimit">Current limitation: single-episode chronological split.</p> | |
| </article> | |
| </div> | |
| <div class="task-selector" id="walkthroughSelector" aria-label="Task walkthrough selector"></div> | |
| </div> | |
| </section> | |
| <section id="tasks" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Task cards and metrics.</h2> | |
| <p>The 12 task cards use readable research names, representative modality thumbnails, explicit input-process-output contracts, and verified minimal versus neural scores from the committed result files.</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" aria-live="polite"></div> | |
| </div> | |
| </section> | |
| <section id="features" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1"> | |
| <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" data-project-tab="results" role="tabpanel" aria-labelledby="tab-results" tabindex="-1"> | |
| <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"> | |
| <img class="chart" src="assets/charts/episode_task_scores_neural_mlp.svg" alt="Neural MLP task score chart"> | |
| <img class="chart" src="assets/charts/episode_task_scores_minimal_vs_neural.svg" alt="Minimal versus neural score chart"> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="artifacts" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Research artifacts for the next experiments.</h2> | |
| <p>Metrics, predictions, manifests, lightweight model weights, and derived window artifacts are organized so the project can be inspected, extended, and scaled before rerunning the full pipeline. Raw Xperience-10M data and Qwen weights are not redistributed.</p> | |
| </div> | |
| <div class="artifact-library"> | |
| <div class="content-tabs" role="tablist" aria-label="Artifact categories"> | |
| <button type="button" class="content-tab active" id="artifact-tab-task-heads" role="tab" data-panel-target="artifact-panel-task-heads" aria-selected="true" aria-pressed="true" aria-controls="artifact-panel-task-heads"> | |
| <strong>Task Heads</strong> | |
| <span>windows, features, metrics</span> | |
| </button> | |
| <button type="button" class="content-tab" id="artifact-tab-public-surfaces" role="tab" data-panel-target="artifact-panel-public-surfaces" aria-selected="false" aria-pressed="false" aria-controls="artifact-panel-public-surfaces" tabindex="-1"> | |
| <strong>Public Surfaces</strong> | |
| <span>repo, HF, project map</span> | |
| </button> | |
| <button type="button" class="content-tab" id="artifact-tab-scale-up" role="tab" data-panel-target="artifact-panel-scale-up" aria-selected="false" aria-pressed="false" aria-controls="artifact-panel-scale-up" tabindex="-1"> | |
| <strong>Scale-Up</strong> | |
| <span>data gate and Omni path</span> | |
| </button> | |
| <button type="button" class="content-tab" id="artifact-tab-checks" role="tab" data-panel-target="artifact-panel-checks" aria-selected="false" aria-pressed="false" aria-controls="artifact-panel-checks" tabindex="-1"> | |
| <strong>Checks</strong> | |
| <span>validators and parity</span> | |
| </button> | |
| </div> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-task-heads" role="tabpanel" aria-labelledby="artifact-tab-task-heads"> | |
| <div class="artifact-group-head"> | |
| <div><span>Research artifacts</span><h3>From one episode to task heads</h3></div> | |
| <p>Start with the files that define the sample windows, feature blocks, task contracts, metrics, walkthroughs, and research-direction mapping.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Task-suite report</h3><p>One JSON file with every task definition, split detail, feature dimension, and minimal/neural metric.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/summary_report.json">summary_report.json</a></article> | |
| <article class="artifact"><h3>Windows table</h3><p>Window start/end frames and aligned action/subtask labels for the public sample episode.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/windows.csv">windows.csv</a></article> | |
| <article class="artifact"><h3>Feature manifest</h3><p>Start/end index and dimension for every current feature block in the 8,378-d window vector.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/episode_task_suite/feature_manifest.json">feature_manifest.json</a></article> | |
| <article class="artifact"><h3>Neural MLP task results</h3><p>Per-task PyTorch MLP metrics, predictions, histories, and checkpoints for the same 12 task contracts.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/neural_mlp">neural_mlp/</a></article> | |
| <article class="artifact"><h3>Four-direction taxonomy</h3><p>Generated JSON, CSV, Markdown, and website data mapping all 12 tasks to the four research tracks.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_directions">research_directions/</a></article> | |
| <article class="artifact"><h3>Direction extension probes</h3><p>Four coded probes, one per research direction, with minimal and neural metrics plus prediction/rank CSVs.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/research_direction_extensions">research_direction_extensions/</a></article> | |
| <article class="artifact"><h3>Task walkthroughs</h3><p>Case studies for all 12 tasks, including input, middle process modules, output, metric, limitation, and task-player data.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/results/episode_task_suite/task_walkthroughs">task_walkthroughs/</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-xperience-10m-task-suite/blob/main/results/episode_task_suite/cross_modal_retrieval/metrics.json">metrics.json</a></article> | |
| </div> | |
| </section> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-public-surfaces" role="tabpanel" aria-labelledby="artifact-tab-public-surfaces" hidden> | |
| <div class="artifact-group-head"> | |
| <div><span>Public surfaces</span><h3>Project map, mirrors, and runnable code</h3></div> | |
| <p>Use these files to navigate the whole project, open the published mirrors, or reproduce the public-sample pipeline.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Artifact guide</h3><p>Human-readable map from project scope to data contract, task evidence, platform mirrors, and scale-up status.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/ARTIFACT_GUIDE.md">ARTIFACT_GUIDE.md</a></article> | |
| <article class="artifact"><h3>Reproduction scripts</h3><p>Training, visualization, taxonomy, walkthrough, validator, and omni-readiness scripts.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/tree/main/scripts">scripts/</a></article> | |
| <article class="artifact"><h3>Hugging Face Space</h3><p>The dashboard packaged as a public static Space.</p><a href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite">HF Space</a></article> | |
| <article class="artifact"><h3>Derived HF artifacts</h3><p>Metrics, predictions, docs, and lightweight derived files without raw data redistribution.</p><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts">dataset repo</a></article> | |
| <article class="artifact"><h3>HF baseline models</h3><p>Minimal NumPy softmax, ridge baselines, and neural task-head model files.</p><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-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-xperience-10m-task-suite">collection</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-xperience-10m-task-suite/blob/main/results/min_all_modalities_action_model/metrics.json">metrics.json</a></article> | |
| <article class="artifact"><h3>Project packet</h3><p>Machine-readable 90-second project path and scope summary.</p><a href="data/project_packet.json">project_packet.json</a></article> | |
| </div> | |
| </section> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-scale-up" role="tabpanel" aria-labelledby="artifact-tab-scale-up" hidden> | |
| <div class="artifact-group-head"> | |
| <div><span>Scale-up path</span><h3>Prepared for multi-episode training</h3></div> | |
| <p>The multi-episode Qwen3-Omni path is documented and scripted. Full-pilot metrics come after the data gate and held-out evaluation pass.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact primary-artifact"><div><h3>Project scope</h3><p>Connects implemented single-episode artifacts, setup-stage Omni work, pending data access, and later multi-episode milestones.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">EVIDENCE_CONTRACT.md</a></article> | |
| <article class="artifact"><h3>Multi-episode access status</h3><p>Public data-access path and selected 32-episode pilot plan, without private infrastructure details.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">MULTI_EPISODE_ACCESS_STATUS.md</a></article> | |
| <article class="artifact"><h3>Qwen3-Omni setup artifacts</h3><p>Manifests, metadata, metrics, and progress logs from the current setup run.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/episode_manifest.json">episode_manifest.json</a></article> | |
| <article class="artifact"><h3>32-episode data gate</h3><p>The data gate defines what must be available before full pilot training and held-out metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">DATA_BLOCKER_REPORT.md</a></article> | |
| </div> | |
| </section> | |
| <section class="artifact-group tabbed-panel" id="artifact-panel-checks" role="tabpanel" aria-labelledby="artifact-tab-checks" hidden> | |
| <div class="artifact-group-head"> | |
| <div><span>Consistency checks</span><h3>Release checks behind the research site</h3></div> | |
| <p>These validator outputs support the public research artifacts by keeping links, mirrors, figures, source wording, and package boundaries consistent.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact"><h3>Artifact index</h3><p>Selective source-of-truth catalog with existence checks, sizes, and stable-file hashes.</p><a href="data/artifact_index.json">artifact_index.json</a></article> | |
| <article class="artifact"><h3>Task-surface integrity</h3><p>Checks that public task cards use readable research names, modality thumbnails, and the interactive walkthrough/player contract.</p><a href="data/task_surface_integrity.json">task_surface_integrity.json</a></article> | |
| <article class="artifact"><h3>Website integrity</h3><p>Checks local links, anchors, JSON files, and referenced website image dimensions.</p><a href="data/website_integrity.json">website_integrity.json</a></article> | |
| <article class="artifact"><h3>Quality gates</h3><p>One release checklist for automated validators and live post-publish checks.</p><a href="data/quality_gates.json">quality_gates.json</a></article> | |
| <article class="artifact"><h3>Mirror parity</h3><p>Prepared repo, HF Space, artifact dataset, and model bundle parity for critical data, figures, website HTML, and validator files.</p><a href="data/mirror_parity.json">mirror_parity.json</a></article> | |
| <article class="artifact"><h3>Live publication</h3><p>Last public GitHub/HF URL verification after upload.</p><a href="data/live_publication_status.json">live_publication_status.json</a></article> | |
| <article class="artifact"><h3>Scale-up status check</h3><p>Machine check that historical <code>32ep</code> identifiers stay in setup-file provenance and are not presented as completed 32-episode results.</p><a href="data/scope_claims_audit.json">scope_claims_audit.json</a></article> | |
| <article class="artifact"><h3>Public presentation check</h3><p>Checks repo, website, and Hugging Face presentation quality, accessibility semantics, public links, and copy consistency.</p><a href="data/public_surface_qa.json">public_surface_qa.json</a></article> | |
| <article class="artifact"><h3>Publication package check</h3><p>Checks raw-data exclusion, cache exclusion, heavy-archive exclusion, token-string scanning, and public-card figure freshness.</p><a href="data/publication_audit.json">publication_audit.json</a></article> | |
| </div> | |
| </section> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="omni-relay" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Qwen3-Omni pilot is approval-ready.</h2> | |
| <p>The full Xperience-10M Hugging Face dataset is gated. While access is pending, the public plan has selected a 32-episode pilot across 32 different session UUIDs.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact"><h3>Selection</h3><p>Stratified round-robin over 64 top-level sessions; 680 complete candidates scanned; 32 sessions selected.</p></article> | |
| <article class="artifact"><h3>Transfer</h3><p>Download raw episodes only from official gated sources, exclude visualization.rrd, validate files, then stage them for training.</p></article> | |
| <article class="artifact"><h3>Boundary</h3><p>The current LoRA artifact is a readiness checkpoint. A real 32-episode result requires local gated data and held-out evaluation.</p></article> | |
| </div> | |
| </div> | |
| </section> | |
| <section id="run" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1"> | |
| <div class="wrap"> | |
| <div class="section-head"> | |
| <h2>Reproduce the suite.</h2> | |
| <p>Raw Xperience-10M data is not redistributed here. The public reproduction contract states the commands, expected outputs, exact-match reproduction evidence, and current non-reproducible scale-up boundary.</p> | |
| </div> | |
| <div class="artifact-grid"> | |
| <article class="artifact"><h3>Reproducibility contract</h3><p>Human-readable commands, expected artifacts, and boundaries for the public single-episode pipeline.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/REPRODUCIBILITY.md">REPRODUCIBILITY.md</a></article> | |
| <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility_matrix.json</a></article> | |
| <article class="artifact"><h3>Exact-match reproduction check</h3><p>The last metric check rebuilt the public-sample outputs from fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproducibility_audit.md</a></article> | |
| <article class="artifact"><h3>Website integrity</h3><p>Local HTML references, anchors, JSON bundles, and image dimensions checked before publishing.</p><a href="data/website_integrity.json">website_integrity.json</a></article> | |
| <article class="artifact"><h3>Scale-up boundary</h3><p>The 32-episode Qwen3-Omni pilot is prepared but not publicly reproducible until gated data access and held-out evaluation pass.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">DATA_BLOCKER_REPORT.md</a></article> | |
| </div> | |
| <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then run the artifact index and publication validator.</p> | |
| <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 | |
| git clone https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite.git | |
| pip install -r ropedia-xperience-10m-task-suite/requirements.txt | |
| pip install torch | |
| hf download ropedia-ai/xperience-10m-sample \ | |
| --repo-type dataset \ | |
| --local-dir data/sample/xperience-10m-sample | |
| cd ropedia-xperience-10m-task-suite | |
| export WORKSPACE=/path/to/workspace | |
| python scripts/episode_task_suite.py --workspace "$WORKSPACE" --include-neural | |
| python scripts/research_direction_extension_tasks.py | |
| python scripts/task_walkthroughs.py | |
| python scripts/generate_visualizations.py | |
| python scripts/render_overview_figures.py | |
| python scripts/render_task_suite_infographic.py | |
| python scripts/export_modality_atlas_assets.py | |
| python scripts/validate_website_integrity.py | |
| python scripts/validate_scope_claims.py | |
| python scripts/build_artifact_index.py | |
| python scripts/validate_mirror_parity.py | |
| python scripts/validate_publication_package.py</code></pre> | |
| </div> | |
| </section> | |
| </main> | |
| <footer> | |
| <div class="wrap"> | |
| Built as a single-episode embodied-AI learning lab, with the next stage focused on multi-episode training and held-out episode evaluation. | |
| </div> | |
| </footer> | |
| <script> | |
| const modalityMeta = { | |
| video: { label: "Video", src: "assets/modalities/video.jpg" }, | |
| audio: { label: "Audio", src: "assets/modalities/audio.png" }, | |
| depth: { label: "Depth", src: "assets/modalities/depth.jpg" }, | |
| pose_slam: { label: "Pose / SLAM", src: "assets/modalities/pose_slam.png" }, | |
| motion_capture: { label: "Motion Capture", src: "assets/modalities/motion_capture.png" }, | |
| inertial: { label: "Inertial", src: "assets/modalities/inertial.png" }, | |
| language: { label: "Language", src: "assets/modalities/language.png" } | |
| }; | |
| const familyAccent = { | |
| supervised: "#9bdfff", | |
| forecast: "#a7f078", | |
| retrieval: "#7ae5c3", | |
| diagnostic: "#d8f4a5" | |
| }; | |
| let taskEntries = []; | |
| let activeTaskIndex = 0; | |
| let activeStageIndex = 0; | |
| let activeFilter = "all"; | |
| let playerTimer = null; | |
| const storyStages = [ | |
| { key: "input", label: "Input" }, | |
| { key: "process", label: "Process" }, | |
| { key: "output", label: "Output" }, | |
| { key: "evaluate", label: "Evaluate" } | |
| ]; | |
| const tabSections = Array.from(document.querySelectorAll("[data-project-tab]")); | |
| const tabButtons = Array.from(document.querySelectorAll("[data-tab-key]")); | |
| const sectionTabs = document.getElementById("sectionTabs"); | |
| const sectionConfig = [ | |
| { id: "overview", label: "Project Overview" }, | |
| { id: "reading-path", label: "Reading Path" }, | |
| { id: "dataset-card", label: "Dataset Card" }, | |
| { id: "suite", label: "12-Task Map" }, | |
| { id: "walkthroughs", label: "Interactive Walkthrough" }, | |
| { id: "tasks", label: "Task Cards" }, | |
| { id: "pipeline", label: "Pipeline" }, | |
| { id: "protocol", label: "Evaluation Protocol" }, | |
| { id: "architectures", label: "Model Architectures" }, | |
| { id: "features", label: "Feature Blocks" }, | |
| { id: "takeaways", label: "Research Takeaways" }, | |
| { id: "models", label: "Minimal Baselines" }, | |
| { id: "neural", label: "Neural Heads" }, | |
| { id: "directions", label: "Four Directions" }, | |
| { id: "extensions", label: "Extension Probes" }, | |
| { id: "diagnostics", label: "Diagnostic Charts" }, | |
| { id: "artifacts", label: "Research Artifacts" }, | |
| { id: "evidence", label: "Research Progress" }, | |
| { id: "omni-relay", label: "Omni Scale-Up" }, | |
| { id: "run", label: "Reproduce" } | |
| ]; | |
| const sectionLabels = Object.fromEntries(sectionConfig.map((section) => [section.id, section.label])); | |
| const sectionTabMap = Object.fromEntries(tabSections.map((section) => [section.id, section.dataset.projectTab])); | |
| const tabDefaultSections = Object.fromEntries( | |
| tabButtons.map((button) => [button.dataset.tabKey, button.dataset.defaultSection]) | |
| ); | |
| function orderedSectionsForTab(tabKey) { | |
| const configured = sectionConfig | |
| .map(({ id }) => document.getElementById(id)) | |
| .filter((section) => section?.dataset.projectTab === tabKey); | |
| const configuredIds = new Set(configured.map((section) => section.id)); | |
| const unconfigured = tabSections.filter((section) => ( | |
| section.dataset.projectTab === tabKey && !configuredIds.has(section.id) | |
| )); | |
| return [...configured, ...unconfigured]; | |
| } | |
| function labelForSection(section) { | |
| return sectionLabels[section.id] || | |
| section.querySelector("h2")?.textContent?.replace(/\.$/, "") || | |
| section.id.replaceAll("-", " "); | |
| } | |
| function renderSectionTabs(tabKey, activeSectionId) { | |
| if (!sectionTabs) return; | |
| const sections = orderedSectionsForTab(tabKey); | |
| sectionTabs.replaceChildren(); | |
| sections.forEach((section, index) => { | |
| const button = document.createElement("button"); | |
| const active = section.id === activeSectionId; | |
| button.type = "button"; | |
| button.className = `section-tab${active ? " active" : ""}`; | |
| button.id = `section-tab-${section.id}`; | |
| button.dataset.sectionTarget = section.id; | |
| button.setAttribute("role", "tab"); | |
| button.setAttribute("aria-controls", section.id); | |
| button.setAttribute("aria-selected", active ? "true" : "false"); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| button.tabIndex = active ? 0 : -1; | |
| button.textContent = labelForSection(section); | |
| button.addEventListener("click", () => { | |
| setProjectTab(tabKey, { | |
| targetId: section.id, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| }); | |
| button.addEventListener("keydown", (event) => { | |
| if (!["ArrowRight", "ArrowDown", "ArrowLeft", "ArrowUp", "Home", "End"].includes(event.key)) return; | |
| event.preventDefault(); | |
| const lastIndex = sections.length - 1; | |
| let nextIndex = index; | |
| if (event.key === "ArrowRight" || event.key === "ArrowDown") nextIndex = index === lastIndex ? 0 : index + 1; | |
| if (event.key === "ArrowLeft" || event.key === "ArrowUp") nextIndex = index === 0 ? lastIndex : index - 1; | |
| if (event.key === "Home") nextIndex = 0; | |
| if (event.key === "End") nextIndex = lastIndex; | |
| const nextSection = sections[nextIndex]; | |
| const nextButton = sectionTabs.querySelector(`[data-section-target="${nextSection.id}"]`); | |
| nextButton?.focus(); | |
| setProjectTab(tabKey, { | |
| targetId: nextSection.id, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| }); | |
| sectionTabs.appendChild(button); | |
| }); | |
| } | |
| function setProjectTab(tabKey, options = {}) { | |
| const fallbackTab = tabDefaultSections[tabKey] ? tabKey : "start"; | |
| const requestedId = options.targetId || tabDefaultSections[fallbackTab] || "overview"; | |
| const targetId = sectionTabMap[requestedId] === fallbackTab | |
| ? requestedId | |
| : tabDefaultSections[fallbackTab] || "overview"; | |
| document.getElementById("main").dataset.activeTab = fallbackTab; | |
| document.getElementById("main").dataset.activeSection = targetId; | |
| tabSections.forEach((section) => { | |
| section.hidden = section.id !== targetId; | |
| }); | |
| tabButtons.forEach((button) => { | |
| const active = button.dataset.tabKey === fallbackTab; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-selected", active ? "true" : "false"); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| button.tabIndex = active ? 0 : -1; | |
| }); | |
| renderSectionTabs(fallbackTab, targetId); | |
| if (options.pushHash) { | |
| history.pushState(null, "", `#${targetId}`); | |
| } | |
| if (options.scroll) { | |
| requestAnimationFrame(() => { | |
| document.getElementById(targetId)?.scrollIntoView({ | |
| behavior: options.smooth ? "smooth" : "auto", | |
| block: "start" | |
| }); | |
| }); | |
| } | |
| } | |
| function activateTabForHash(options = {}) { | |
| const hashId = decodeURIComponent(window.location.hash.replace(/^#/, "")); | |
| const tabKey = sectionTabMap[hashId] || "start"; | |
| const targetId = sectionTabMap[hashId] ? hashId : tabDefaultSections[tabKey]; | |
| setProjectTab(tabKey, { targetId, scroll: options.scroll, smooth: options.smooth }); | |
| } | |
| function moveProjectTabFocus(currentIndex, key) { | |
| const lastIndex = tabButtons.length - 1; | |
| let nextIndex = currentIndex; | |
| if (key === "ArrowRight" || key === "ArrowDown") nextIndex = currentIndex === lastIndex ? 0 : currentIndex + 1; | |
| if (key === "ArrowLeft" || key === "ArrowUp") nextIndex = currentIndex === 0 ? lastIndex : currentIndex - 1; | |
| if (key === "Home") nextIndex = 0; | |
| if (key === "End") nextIndex = lastIndex; | |
| const nextButton = tabButtons[nextIndex]; | |
| nextButton.focus(); | |
| setProjectTab(nextButton.dataset.tabKey, { | |
| targetId: nextButton.dataset.defaultSection, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| } | |
| tabButtons.forEach((button, index) => { | |
| button.addEventListener("click", () => { | |
| setProjectTab(button.dataset.tabKey, { | |
| targetId: button.dataset.defaultSection, | |
| pushHash: true, | |
| scroll: true, | |
| smooth: true | |
| }); | |
| }); | |
| button.addEventListener("keydown", (event) => { | |
| if (!["ArrowRight", "ArrowDown", "ArrowLeft", "ArrowUp", "Home", "End"].includes(event.key)) return; | |
| event.preventDefault(); | |
| moveProjectTabFocus(index, event.key); | |
| }); | |
| }); | |
| window.addEventListener("hashchange", () => activateTabForHash({ scroll: true })); | |
| activateTabForHash({ scroll: Boolean(window.location.hash) }); | |
| function initContentTabs() { | |
| document.querySelectorAll(".content-tabs").forEach((tablist) => { | |
| const buttons = Array.from(tablist.querySelectorAll("[data-panel-target]")); | |
| if (!buttons.length) return; | |
| const activatePanel = (activeButton, options = {}) => { | |
| buttons.forEach((button) => { | |
| const active = button === activeButton; | |
| const panel = document.getElementById(button.dataset.panelTarget); | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-selected", active ? "true" : "false"); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| button.tabIndex = active ? 0 : -1; | |
| if (panel) panel.hidden = !active; | |
| }); | |
| if (options.focus) activeButton.focus(); | |
| }; | |
| buttons.forEach((button, index) => { | |
| button.addEventListener("click", () => activatePanel(button)); | |
| button.addEventListener("keydown", (event) => { | |
| if (!["ArrowRight", "ArrowDown", "ArrowLeft", "ArrowUp", "Home", "End"].includes(event.key)) return; | |
| event.preventDefault(); | |
| const lastIndex = buttons.length - 1; | |
| let nextIndex = index; | |
| if (event.key === "ArrowRight" || event.key === "ArrowDown") nextIndex = index === lastIndex ? 0 : index + 1; | |
| if (event.key === "ArrowLeft" || event.key === "ArrowUp") nextIndex = index === 0 ? lastIndex : index - 1; | |
| if (event.key === "Home") nextIndex = 0; | |
| if (event.key === "End") nextIndex = lastIndex; | |
| activatePanel(buttons[nextIndex], { focus: true }); | |
| }); | |
| }); | |
| activatePanel(buttons.find((button) => button.classList.contains("active")) || buttons[0]); | |
| }); | |
| } | |
| initContentTabs(); | |
| const escapeHtml = (value) => String(value ?? "") | |
| .replaceAll("&", "&") | |
| .replaceAll("<", "<") | |
| .replaceAll(">", ">") | |
| .replaceAll('"', """) | |
| .replaceAll("'", "'"); | |
| const formatMetric = (value) => { | |
| if (value === null || value === undefined || Number.isNaN(Number(value))) return "n/a"; | |
| const numeric = Number(value); | |
| if (Math.abs(numeric) >= 10) return numeric.toFixed(2); | |
| return numeric.toFixed(4); | |
| }; | |
| const metricBarWidth = (task) => { | |
| const value = Number(task.metric?.neural_mlp ?? task.metric?.minimal ?? 0); | |
| if (!Number.isFinite(value)) return 4; | |
| if (task.metric?.name === "R2") return Math.max(4, Math.min(100, (value + 1) * 50)); | |
| if (task.metric?.direction === "lower") return Math.max(4, Math.min(100, 100 / (1 + Math.max(value, 0)))); | |
| return Math.max(4, Math.min(100, value * 100)); | |
| }; | |
| const normalizeTasks = (payload) => Object.values(payload.tasks || {}); | |
| const modalityLabels = (task) => (task.modalities || []) | |
| .map((key) => modalityMeta[key]?.label) | |
| .filter(Boolean) | |
| .join(", "); | |
| function stageNarration(task) { | |
| const minimal = formatMetric(task.metric?.minimal); | |
| const neural = formatMetric(task.metric?.neural_mlp); | |
| const modules = (task.middle_modules || []).slice(0, 2).join(" "); | |
| return [ | |
| `Input: ${task.input_short}. Evidence shown here comes from ${modalityLabels(task)}.`, | |
| `Process: ${task.process_short}. ${modules}`, | |
| `Output: ${task.output_short}. Case study: ${task.case_study}`, | |
| `Evaluate: ${task.metric.name}, ${task.metric.direction} is better. Minimal ${minimal}; neural MLP ${neural}. Limitation: ${task.failure_mode}` | |
| ]; | |
| } | |
| function renderTaskCards() { | |
| const grid = document.getElementById("taskGrid"); | |
| grid.innerHTML = taskEntries.map((task, index) => { | |
| const family = task.task_family; | |
| const poster = modalityMeta[task.poster_modality] || modalityMeta.video; | |
| const minimal = formatMetric(task.metric?.minimal); | |
| const neural = formatMetric(task.metric?.neural_mlp); | |
| return ` | |
| <button type="button" class="task-card" data-kind="${escapeHtml(family)}" data-index="${index}" aria-pressed="false"> | |
| <span class="task-card-media" aria-hidden="true"> | |
| <img src="${poster.src}" alt=""> | |
| </span> | |
| <div class="task-top"> | |
| <span> | |
| <span class="task-name">${escapeHtml(task.display_name)}</span> | |
| <span class="task-research-name">${escapeHtml(task.research_name)}</span> | |
| </span> | |
| <span class="tag ${escapeHtml(family)}">${escapeHtml(family)}</span> | |
| </div> | |
| <p>${escapeHtml(task.card_blurb)}</p> | |
| <div class="task-contract"> | |
| <span><strong>Input</strong>${escapeHtml(task.input_short)}</span> | |
| <span><strong>Middle</strong>${escapeHtml(task.process_short)}</span> | |
| <span><strong>Output</strong>${escapeHtml(task.output_short)}</span> | |
| </div> | |
| <div class="metric-row"> | |
| <span><strong>${minimal}</strong>minimal ${escapeHtml(task.metric?.name)}</span> | |
| <span><strong>${neural}</strong>neural MLP</span> | |
| </div> | |
| <div class="mini-bar"><span style="--w:${metricBarWidth(task).toFixed(1)}%;--c:${familyAccent[family] || "#a7f078"}"></span></div> | |
| </button> | |
| `; | |
| }).join(""); | |
| grid.querySelectorAll(".task-card").forEach((card) => { | |
| card.addEventListener("click", () => { | |
| pausePlayer(); | |
| setActiveTask(Number(card.dataset.index)); | |
| document.getElementById("walkthroughs").scrollIntoView({ behavior: "smooth", block: "start" }); | |
| }); | |
| }); | |
| applyTaskFilter(activeFilter); | |
| } | |
| function renderSelector() { | |
| const selector = document.getElementById("walkthroughSelector"); | |
| selector.innerHTML = taskEntries.map((task, index) => ` | |
| <button type="button" class="selector-button" data-index="${index}" aria-pressed="false"> | |
| <strong>${escapeHtml(task.display_name)}</strong> | |
| <span>${escapeHtml(task.task_family)}</span> | |
| </button> | |
| `).join(""); | |
| selector.querySelectorAll(".selector-button").forEach((button) => { | |
| button.addEventListener("click", () => { | |
| pausePlayer(); | |
| setActiveTask(Number(button.dataset.index)); | |
| }); | |
| }); | |
| } | |
| function renderPlayer(task, index) { | |
| const poster = modalityMeta[task.poster_modality] || modalityMeta.video; | |
| document.getElementById("playerPoster").src = poster.src; | |
| document.getElementById("playerPoster").alt = `${task.display_name} representative ${poster.label} modality`; | |
| document.getElementById("playerBadgeTitle").textContent = task.display_name; | |
| document.getElementById("playerBadgeMeta").textContent = task.research_name; | |
| document.getElementById("playerFamily").className = `tag ${task.task_family}`; | |
| document.getElementById("playerFamily").textContent = task.task_family; | |
| document.getElementById("playerArchitecture").textContent = task.architecture_family; | |
| document.getElementById("playerTitle").textContent = task.display_name; | |
| document.getElementById("playerCase").textContent = task.case_study; | |
| document.getElementById("playerInput").textContent = task.input_short; | |
| document.getElementById("playerProcess").textContent = task.process_short; | |
| document.getElementById("playerOutput").textContent = task.output_short; | |
| document.getElementById("playerModules").innerHTML = task.middle_modules.map((module, moduleIndex) => ( | |
| `<li><strong>Module ${String(moduleIndex + 1).padStart(2, "0")}</strong>${escapeHtml(module)}</li>` | |
| )).join(""); | |
| document.getElementById("playerMetric").textContent = `${task.metric.name} (${task.metric.direction} is better). Minimal ${formatMetric(task.metric.minimal)}; neural MLP ${formatMetric(task.metric.neural_mlp)}.`; | |
| document.getElementById("playerLimit").textContent = `Current limitation: ${task.failure_mode}`; | |
| document.getElementById("playerScrub").max = Math.max(0, taskEntries.length - 1); | |
| document.getElementById("playerScrub").value = index; | |
| document.getElementById("playerModalities").innerHTML = task.modalities.map((key) => { | |
| const modality = modalityMeta[key] || modalityMeta.video; | |
| return `<span class="modality-tile"><img src="${modality.src}" alt="${escapeHtml(modality.label)} sample thumbnail"><span>${escapeHtml(modality.label)}</span></span>`; | |
| }).join(""); | |
| renderStageFrame(task, index); | |
| } | |
| function renderStageFrame(task, index) { | |
| const stage = storyStages[activeStageIndex] || storyStages[0]; | |
| const narration = stageNarration(task); | |
| const totalFrames = Math.max(1, taskEntries.length * storyStages.length); | |
| const currentFrame = index * storyStages.length + activeStageIndex + 1; | |
| document.getElementById("playerFrameChip").textContent = `Step ${activeStageIndex + 1} / ${storyStages.length} · ${stage.label}`; | |
| document.getElementById("playerFrameCaption").textContent = narration[activeStageIndex] || narration[0]; | |
| document.getElementById("playerCounter").textContent = `${String(index + 1).padStart(2, "0")} / ${String(taskEntries.length).padStart(2, "0")} · ${stage.label}`; | |
| document.getElementById("playerProgress").style.width = `${(currentFrame / totalFrames) * 100}%`; | |
| document.querySelectorAll("[data-stage]").forEach((button) => { | |
| const active = Number(button.dataset.stage) === activeStageIndex; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| } | |
| function updateActiveMarkers() { | |
| document.querySelectorAll(".task-card").forEach((card) => { | |
| const active = Number(card.dataset.index) === activeTaskIndex; | |
| card.classList.toggle("active", active); | |
| card.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| document.querySelectorAll(".selector-button").forEach((button) => { | |
| const active = Number(button.dataset.index) === activeTaskIndex; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| } | |
| function setActiveTask(index, options = {}) { | |
| if (!taskEntries.length) return; | |
| activeTaskIndex = (index + taskEntries.length) % taskEntries.length; | |
| if (options.resetStage !== false) activeStageIndex = 0; | |
| renderPlayer(taskEntries[activeTaskIndex], activeTaskIndex); | |
| updateActiveMarkers(); | |
| } | |
| function setActiveStage(index) { | |
| if (!taskEntries.length) return; | |
| activeStageIndex = (index + storyStages.length) % storyStages.length; | |
| renderStageFrame(taskEntries[activeTaskIndex], activeTaskIndex); | |
| } | |
| function advancePlayer() { | |
| if (activeStageIndex < storyStages.length - 1) { | |
| setActiveStage(activeStageIndex + 1); | |
| return; | |
| } | |
| setActiveTask(activeTaskIndex + 1); | |
| } | |
| function applyTaskFilter(filter) { | |
| activeFilter = filter; | |
| document.querySelectorAll(".filter").forEach((button) => { | |
| const active = button.dataset.filter === filter; | |
| button.classList.toggle("active", active); | |
| button.setAttribute("aria-pressed", active ? "true" : "false"); | |
| }); | |
| document.querySelectorAll(".task-card").forEach((card) => { | |
| card.classList.toggle("hide", filter !== "all" && card.dataset.kind !== filter); | |
| }); | |
| } | |
| function pausePlayer() { | |
| if (playerTimer) { | |
| window.clearInterval(playerTimer); | |
| playerTimer = null; | |
| document.getElementById("playerPlay").textContent = "Play"; | |
| } | |
| } | |
| function togglePlayer() { | |
| if (playerTimer) { | |
| pausePlayer(); | |
| return; | |
| } | |
| document.getElementById("playerPlay").textContent = "Pause"; | |
| playerTimer = window.setInterval(advancePlayer, 2600); | |
| } | |
| async function initTaskSurface() { | |
| try { | |
| const response = await fetch("data/task_walkthroughs.json", { cache: "no-cache" }); | |
| if (!response.ok) throw new Error(`task data ${response.status}`); | |
| taskEntries = normalizeTasks(await response.json()); | |
| renderTaskCards(); | |
| renderSelector(); | |
| setActiveTask(0); | |
| } catch (error) { | |
| document.getElementById("taskGrid").innerHTML = '<p class="repro-note">Task data could not be loaded from data/task_walkthroughs.json.</p>'; | |
| document.getElementById("walkthroughSelector").innerHTML = ""; | |
| } | |
| } | |
| document.querySelectorAll(".filter").forEach((button) => { | |
| button.addEventListener("click", () => applyTaskFilter(button.dataset.filter)); | |
| }); | |
| document.getElementById("playerPrev").addEventListener("click", () => { pausePlayer(); setActiveTask(activeTaskIndex - 1); }); | |
| document.getElementById("playerNext").addEventListener("click", () => { pausePlayer(); setActiveTask(activeTaskIndex + 1); }); | |
| document.getElementById("playerPlay").addEventListener("click", togglePlayer); | |
| document.getElementById("playerScrub").addEventListener("input", (event) => { | |
| pausePlayer(); | |
| setActiveTask(Number(event.target.value)); | |
| }); | |
| document.querySelectorAll("[data-stage]").forEach((button) => { | |
| button.addEventListener("click", () => { | |
| pausePlayer(); | |
| setActiveStage(Number(button.dataset.stage)); | |
| }); | |
| }); | |
| initTaskSurface(); | |
| document.querySelectorAll("[data-copy]").forEach((button) => { | |
| button.addEventListener("click", async () => { | |
| const target = document.getElementById(button.dataset.copy); | |
| try { | |
| await navigator.clipboard.writeText(target.innerText); | |
| } catch (error) { | |
| return; | |
| } | |
| const previous = button.textContent; | |
| button.textContent = "Copied"; | |
| setTimeout(() => button.textContent = previous, 1300); | |
| }); | |
| }); | |
| </script> | |
| </body> | |
| </html> | |