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Publish Ropedia Xperience-10M derived artifacts
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<title>Ropedia Xperience-10M Task Suite</title>
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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) {
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<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>
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<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>
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<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 "$WORKSPACE" --include-neural
python scripts/research_direction_extension_tasks.py
python scripts/task_walkthroughs.py
python scripts/generate_visualizations.py
python scripts/render_overview_figures.py
python scripts/render_task_suite_infographic.py
python scripts/export_modality_atlas_assets.py
python scripts/validate_website_integrity.py
python scripts/validate_scope_claims.py
python scripts/build_artifact_index.py
python scripts/validate_mirror_parity.py
python scripts/validate_publication_package.py</code></pre>
</div>
</section>
</main>
<footer>
<div class="wrap">
Built as a single-episode embodied-AI learning lab, with the next stage focused on multi-episode training and held-out episode evaluation.
</div>
</footer>
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const fallbackTab = tabDefaultSections[tabKey] ? tabKey : "start";
const requestedId = options.targetId || tabDefaultSections[fallbackTab] || "overview";
const targetId = sectionTabMap[requestedId] === fallbackTab
? requestedId
: tabDefaultSections[fallbackTab] || "overview";
document.getElementById("main").dataset.activeTab = fallbackTab;
document.getElementById("main").dataset.activeSection = targetId;
tabSections.forEach((section) => {
section.hidden = section.id !== targetId;
});
tabButtons.forEach((button) => {
const active = button.dataset.tabKey === fallbackTab;
button.classList.toggle("active", active);
button.setAttribute("aria-selected", active ? "true" : "false");
button.setAttribute("aria-pressed", active ? "true" : "false");
button.tabIndex = active ? 0 : -1;
});
renderSectionTabs(fallbackTab, targetId);
if (options.pushHash) {
history.pushState(null, "", `#${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("&", "&amp;")
.replaceAll("<", "&lt;")
.replaceAll(">", "&gt;")
.replaceAll('"', "&quot;")
.replaceAll("'", "&#039;");
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>