Spaces:
Running
Running
File size: 25,202 Bytes
2f9cbaa 61e9d5f 2f9cbaa | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 | <!doctype html>
<html lang="en">
<head>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1">
<meta name="theme-color" content="#fdfdfb">
<meta name="description" content="DVIDIA Training research alpha: a local CPU training pipeline, synthetic visual baseline measurements, and one recorded native simulation qualification. Training runs locally; this Space presents published evidence.">
<title>DVIDIA Training Β· Published pilot</title>
<style>
:root {
color-scheme: light;
--paper: #fdfdfb;
--ink: #262623;
--muted: #696963;
--rule: #dcdcd5;
--rule-dark: #b7b7ae;
--blue: #3c6cff;
--serif: Georgia, "Times New Roman", Times, serif;
--sans: -apple-system, BlinkMacSystemFont, "Segoe UI", sans-serif;
--mono: ui-monospace, SFMono-Regular, Consolas, "Liberation Mono", monospace;
--sidebar: 220px;
background: var(--paper);
color: var(--ink);
font-family: var(--serif);
font-synthesis: none;
text-rendering: optimizeLegibility;
}
* { box-sizing: border-box; }
html { scroll-padding-top: 28px; }
body { margin: 0; font-size: 17px; line-height: 1.72; }
body::before {
content: "";
position: fixed;
inset: 0;
z-index: -1;
pointer-events: none;
opacity: .045;
background-image: url("data:image/svg+xml,%3Csvg xmlns='http://www.w3.org/2000/svg' width='180' height='180'%3E%3Cfilter id='grain'%3E%3CfeTurbulence type='fractalNoise' baseFrequency='.8' numOctaves='3' stitchTiles='stitch'/%3E%3C/filter%3E%3Crect width='100%25' height='100%25' opacity='.7' filter='url(%23grain)'/%3E%3C/svg%3E");
}
a { color: inherit; text-underline-offset: .2em; text-decoration-thickness: 1px; text-decoration-color: #92928a; }
a:hover { text-decoration-color: currentColor; }
:where(a, summary, iframe):focus-visible { outline: 2px solid var(--blue); outline-offset: 5px; }
::selection { background: #e6e9f6; }
img { display: block; max-width: 100%; height: auto; }
h1, h2, h3 { color: #22221f; text-wrap: balance; }
p, li { text-wrap: pretty; }
p { margin: 0 0 1em; }
.skip-link { position: fixed; top: 12px; left: 12px; z-index: 10; transform: translateY(-180%); padding: 9px 15px; border: 1px solid var(--ink); background: var(--paper); font: 12px/1.6 var(--mono); }
.skip-link:focus { transform: translateY(0); }
.sidebar { position: fixed; inset: 0 auto 0 0; width: var(--sidebar); padding: 32px 23px 24px; border-right: 1px solid var(--rule); overflow-y: auto; font: 11px/1.7 var(--mono); }
.brand { display: flex; align-items: center; gap: 10px; margin-bottom: 36px; text-decoration: none; }
.brand svg { flex: 0 0 auto; width: 29px; height: 29px; }
.brand-word { display: block; font: 600 20px/1.2 var(--sans); letter-spacing: -.055em; }
.brand-tag { display: block; margin-top: 4px; font-size: 9px; color: var(--muted); }
.nav-label { margin: 0 0 8px; color: var(--muted); font: 9px/1.6 var(--mono); letter-spacing: .1em; text-transform: uppercase; }
.sidebar nav + nav { margin-top: 29px; }
.sidebar ul { list-style: none; margin: 0; padding: 0; }
.sidebar li { margin: 0; }
.sidebar nav a { display: block; padding: 7px 0; text-decoration: none; color: #51514b; }
.sidebar nav a:hover { color: var(--ink); text-decoration: underline; }
.sidebar-note { margin-top: 32px; padding-top: 17px; border-top: 1px solid var(--rule); color: var(--muted); font-size: 10px; }
.sidebar-note p { margin-bottom: 7px; }
.page { margin-left: var(--sidebar); padding: 44px 54px 30px; }
.paper { max-width: 1000px; margin: 0 auto; }
.banner { margin: 0 0 33px; border: 1px solid #e8e8e1; }
.banner img { width: 100%; }
.eyebrow { margin: 0 0 13px; color: var(--muted); font: 10px/1.7 var(--mono); letter-spacing: .075em; text-transform: uppercase; }
.paper-header { max-width: 760px; margin-bottom: 35px; }
h1 { margin: 0 0 20px; font-size: clamp(34px, 4vw, 49px); line-height: 1.16; font-weight: 400; letter-spacing: -.035em; }
.intro { max-width: 64ch; font-size: 19px; line-height: 1.65; }
.meta { color: var(--muted); font: 10px/1.8 var(--mono); }
.header-links { display: flex; flex-wrap: wrap; gap: 9px 22px; margin-top: 22px; font: 11px/1.8 var(--mono); }
.scope { margin: 0 0 42px; padding: 20px 0; border-block: 1px solid var(--rule-dark); font-size: 16px; }
.scope p { max-width: 78ch; margin: 0; }
.scope strong { font-weight: 700; }
.section { padding-top: 10px; margin-bottom: 49px; scroll-margin-top: 25px; }
.section-heading { display: flex; align-items: baseline; gap: 15px; margin-bottom: 17px; }
.section-number { color: var(--muted); font: 10px/1.5 var(--mono); }
h2 { margin: 0; font-size: 28px; line-height: 1.3; font-weight: 400; letter-spacing: -.02em; }
h3 { margin: 25px 0 10px; font-size: 19px; line-height: 1.4; font-weight: 400; }
.section > p, .section > ul, .section > ol { max-width: 78ch; }
.section ul, .section ol { padding-left: 1.4em; }
.section li { margin-bottom: .5em; }
.metrics { display: grid; grid-template-columns: repeat(4, 1fr); gap: 18px; margin: 25px 0 19px; padding: 18px 0 19px; border-block: 1px solid var(--rule); }
.metrics div { min-width: 0; }
.metrics dt { color: var(--muted); font: 9px/1.6 var(--mono); }
.metrics dd { margin: 7px 0 0; font-size: 27px; line-height: 1.15; letter-spacing: -.03em; }
.metrics dd small { font-size: 14px; letter-spacing: 0; }
.table-wrap { overflow-x: auto; margin: 23px 0 14px; }
table { width: 100%; border-collapse: collapse; border-block: 1px solid var(--rule-dark); font-size: 15px; line-height: 1.55; }
caption { padding: 0 0 12px; text-align: left; color: var(--muted); font: 10px/1.6 var(--mono); }
th, td { padding: 12px 20px 12px 0; text-align: left; vertical-align: top; }
th:last-child, td:last-child { padding-right: 0; }
thead { border-bottom: 1px solid var(--rule-dark); }
th { font-weight: 700; }
tbody tr + tr { border-top: 1px solid var(--rule); }
.numeric { white-space: nowrap; font: 12px/1.8 var(--mono); }
.note { color: var(--muted); font: 11px/1.85 var(--mono); }
.note a { color: inherit; }
.evidence-links { display: flex; flex-wrap: wrap; gap: 10px 22px; margin-top: 20px; font: 11px/1.8 var(--mono); }
.replay { margin: 24px 0 0; }
.replay iframe { display: block; width: 100%; height: 850px; border: 1px solid var(--rule); background: #f7f7f2; }
.replay figcaption { margin-top: 11px; color: var(--muted); font: 11px/1.8 var(--mono); }
pre { padding: 19px 21px; margin: 18px 0 15px; overflow-x: auto; border: 1px solid var(--rule); background: #f7f7f3; line-height: 1.65; tab-size: 2; }
code { font: 12px/1.65 var(--mono); }
:not(pre) > code { padding: .1em .25em; background: #eeeeea; font-size: .78em; overflow-wrap: anywhere; }
.download-list { margin: 21px 0 0; padding: 0; border-top: 1px solid var(--rule-dark); }
.download-list div { display: grid; grid-template-columns: 190px 1fr; gap: 22px; padding: 17px 0; border-bottom: 1px solid var(--rule); }
.download-list dt { font: 12px/1.7 var(--mono); }
.download-list dd { margin: 0; color: #5a5a53; font-size: 15px; }
.limit-list { margin: 19px 0 0; }
.footer { display: flex; justify-content: space-between; flex-wrap: wrap; gap: 10px 24px; margin-top: 57px; padding-top: 18px; border-top: 1px solid var(--rule); color: var(--muted); font: 10px/1.8 var(--mono); }
.footer p { margin: 0; }
@media (min-width: 1500px) { .page { padding-top: 60px; } }
@media (max-width: 1100px) {
:root { --sidebar: 200px; }
.page { padding: 34px 32px 27px; }
.sidebar { padding-inline: 20px; }
.metrics { grid-template-columns: repeat(2, 1fr); gap: 21px; }
}
@media (max-width: 760px) {
.sidebar { position: static; width: auto; border-right: 0; border-bottom: 1px solid var(--rule); padding: 20px 23px 16px; overflow: visible; }
.brand { margin-bottom: 18px; }
.sidebar nav + nav, .sidebar-note, .nav-label { display: none; }
.sidebar nav ul { display: flex; flex-wrap: wrap; gap: 2px 19px; }
.sidebar nav a { padding: 7px 0; }
.page { margin: 0; padding: 25px 23px 25px; }
.banner { margin-bottom: 24px; }
.paper-header { margin-bottom: 27px; }
h1 { font-size: 36px; }
.intro { font-size: 18px; }
.scope { margin-bottom: 31px; }
.section { margin-bottom: 39px; }
h2 { font-size: 25px; }
.section-heading { gap: 11px; }
.download-list div { grid-template-columns: 1fr; gap: 4px; }
.replay iframe { height: 980px; }
pre { padding: 16px 17px; }
.footer { margin-top: 42px; }
}
@media (max-width: 380px) {
.page, .sidebar { padding-inline: 18px; }
h1 { font-size: 31px; }
.metrics dd { font-size: 25px; }
}
@media print {
:root { --paper: #fff; --ink: #000; --muted: #333; }
body { font-size: 11pt; }
body::before, .sidebar, .skip-link, .banner, .replay iframe { display: none; }
.page { margin: 0; padding: 0; }
.paper { max-width: none; }
.section { break-inside: avoid; }
h1 { font-size: 27pt; }
h2 { font-size: 18pt; }
pre { white-space: pre-wrap; }
}
</style>
</head>
<body>
<a class="skip-link" href="#main">Skip to content</a>
<aside class="sidebar" aria-label="Pilot navigation">
<a class="brand" href="#main" aria-label="DVIDIA Training, published pilot">
<svg viewBox="0 0 64 64" aria-hidden="true">
<rect width="64" height="64" rx="16" fill="#3c6cff"/>
<g fill="#f7f6f2">
<rect x="8" y="20" width="4.6" height="24" rx="1.2" opacity=".28"/>
<rect x="14.1" y="20" width="4.6" height="24" rx="1.2" opacity=".38"/>
<rect x="20.2" y="20" width="4.6" height="24" rx="1.2" opacity=".48"/>
<rect x="26.3" y="20" width="4.6" height="24" rx="1.2" opacity=".58"/>
<rect x="32.4" y="20" width="4.6" height="24" rx="1.2" opacity=".68"/>
<rect x="38.5" y="20" width="4.6" height="24" rx="1.2" opacity=".78"/>
<rect x="44.6" y="20" width="4.6" height="24" rx="1.2" opacity=".9"/>
<rect x="50.7" y="20" width="4.6" height="24" rx="1.2" fill="#fff"/>
</g>
</svg>
<span><span class="brand-word">DVIDIA</span><span class="brand-tag">Open physical skills</span></span>
</a>
<nav aria-label="On this page">
<p class="nav-label">Training pilot</p>
<ul>
<li><a href="#pipeline">Local pipeline</a></li>
<li><a href="#visual">Visual baseline</a></li>
<li><a href="#movement">Movement candidate</a></li>
<li><a href="#replay">Recorded qualification</a></li>
<li><a href="#install">Run locally</a></li>
<li><a href="#artifacts">Published artifacts</a></li>
<li><a href="#limits">Research limits</a></li>
</ul>
</nav>
<nav aria-label="Project links">
<p class="nav-label">Follow the work</p>
<ul>
<li><a href="https://github.com/Dvidia-Inference/dvidia-training">GitHub source β</a></li>
<li><a href="https://research.dvidia.org/papers/skillspace-footage-training/">Research paper β</a></li>
<li><a href="https://dvidia.org">DVIDIA β</a></li>
</ul>
</nav>
<div class="sidebar-note">
<p>Research alpha</p>
<p>Simulation-only evidence<br>MIT licensed</p>
<p>Published pilot Β· 8 Oct 2026</p>
</div>
</aside>
<main class="page" id="main">
<article class="paper" aria-labelledby="page-title">
<figure class="banner">
<img src="assets/branding/github-header.png" alt="DVIDIA Training β toward installable robot skills. Open source, offline, CPU." width="2172" height="724" fetchpriority="high">
</figure>
<header class="paper-header">
<p class="eyebrow">DVIDIA Training Β· Research alpha Β· Simulation only</p>
<h1 id="page-title">Local training.<br>Published evidence.</h1>
<p class="intro">A small CPU pipeline for training visual predictors from Skillspace footage, with an optional movement branch for aligned action labels.</p>
<p class="meta">Published pilot Β· <time datetime="2026-10-08">8 October 2026</time> Β· Toward installable robot skills</p>
<div class="header-links">
<a href="#install">Install the local tool</a>
<a href="#replay">Inspect the recorded replay</a>
<a href="https://github.com/Dvidia-Inference/dvidia-training">View the source β</a>
</div>
</header>
<aside class="scope" aria-label="What this Space provides">
<p><strong>Train on your own computer or server.</strong> This Space shows fixed, published measurements and a recorded simulation replay. It offers no hosted training or uploads. The current pilot establishes neither a hardware skill nor a bridge from raw human video to robot actions.</p>
</aside>
<section class="section" id="pipeline" aria-labelledby="pipeline-title">
<div class="section-heading"><span class="section-number">01</span><h2 id="pipeline-title">A local, inspectable pipeline</h2></div>
<p>The tool checks actual videos and connected source groups, freezes training/development/test splits, fits a small model on a CPU, and exports learned weights with held-out measurements and integrity hashes.</p>
<p>The base learner needs Python 3.12+, NumPy, and FFmpeg with <code>ffprobe</code>. It requires no GPU, CUDA, account, or API key. Training runs offline by default; installing software and downloading public data are separate steps.</p>
<p>Visual fitting and movement fitting have different inputs. Footage trains the visual predictor. The optional movement branch requires compatible, validated numerical action supervision and a task adapter.</p>
<div class="evidence-links">
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/docs/pipeline.md">Pipeline documentation β</a>
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/docs/skillspace-format.md">Dataset and action contracts β</a>
</div>
</section>
<section class="section" id="visual" aria-labelledby="visual-title">
<div class="section-heading"><span class="section-number">02</span><h2 id="visual-title">Synthetic visual baseline</h2></div>
<p>Ten explicitly synthetic moving-disc videos exercise intake, fitting, evaluation, and export. PCA is fitted on training footage only; a ridge next-frame predictor uses development groups to select regularization, then scores held-out footage.</p>
<dl class="metrics">
<div><dt>Synthetic clips</dt><dd>10</dd></div>
<div><dt>Source duration</dt><dd>60 <small>seconds</small></dd></div>
<div><dt>Median pipeline time</dt><dd>0.5106 <small>seconds</small></dd></div>
<div><dt>Train / dev / test groups</dt><dd>7 / 2 / 1</dd></div>
</dl>
<div class="table-wrap">
<table>
<caption>Held-out visual error Β· lower is better</caption>
<thead><tr><th scope="col">Predictor</th><th scope="col">RGB mean squared error</th></tr></thead>
<tbody>
<tr><th scope="row">Learned next-frame predictor</th><td class="numeric">0.0003038793</td></tr>
<tr><th scope="row">Last-frame baseline</th><td class="numeric">0.0017714138</td></tr>
<tr><th scope="row">Training-mean frame baseline</th><td class="numeric">0.0013307309</td></tr>
</tbody>
</table>
</div>
<p class="note">Three pipeline repeats on one Apple M5 macOS host with 24 GiB RAM; the same held-out recording, containing 11 correlated transitions, was re-scored. Timing covers intake, fitting, and export. Software installation, imports, synthetic source creation, and the separate integrity inspection are excluded. This is a small software-throughput measurement, not a robotics success rate or a minimum hardware requirement.</p>
<div class="evidence-links">
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/benchmarks/macos-m5.json">Benchmark receipt β</a>
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/docs/hardware.md">Hardware scope and checks β</a>
</div>
</section>
<section class="section" id="movement" aria-labelledby="movement-title">
<div class="section-heading"><span class="section-number">03</span><h2 id="movement-title">A learned movement candidate</h2></div>
<p>A separate native simulation pilot fits a six-joint delta head from aligned simulator state and authored joint targets. The training set contains 4,029 numerical labels across seven recordings; development uses two recordings, and one recording is held out.</p>
<div class="table-wrap">
<table>
<caption>Held-out joint-delta error Β· 621 samples from one recording</caption>
<thead><tr><th scope="col">Movement predictor</th><th scope="col">Mean squared error</th></tr></thead>
<tbody>
<tr><th scope="row">Learned joint-delta head</th><td class="numeric">5.7679 Γ 10<sup>β7</sup></td></tr>
<tr><th scope="row">Training-mean delta baseline</th><td class="numeric">7.8429 Γ 10<sup>β5</sup></td></tr>
<tr><th scope="row">Zero-delta baseline</th><td class="numeric">8.1727 Γ 10<sup>β5</sup></td></tr>
</tbody>
</table>
</div>
<p>The candidate retains authored task phases, gripper/contact logic, and recovery. Control uses privileged simulator observations; camera perception is not used to drive the arm. The labels were not inferred from human video.</p>
<div class="evidence-links">
<a href="https://huggingface.co/Dvidia/dvidia-training-pilot">Inspect the pilot artifacts β</a>
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/benchmarks/native_pilot.py">Reproduction script β</a>
</div>
</section>
<section class="section" id="replay" aria-labelledby="replay-title">
<div class="section-heading"><span class="section-number">04</span><h2 id="replay-title">One held-out scene, replayed</h2></div>
<p>The candidate is installed into the bounded native placement adapter and tested in the exact scene of the held-out synthetic recording. The arm establishes contact, lifts the free box, and places it at the target. A control replay with identical joint targets and open jaws fails the placement test.</p>
<div class="table-wrap">
<table>
<caption>Independent native qualification Β· one exact simulation scene</caption>
<thead><tr><th scope="col">Run</th><th scope="col">Recorded outcome</th></tr></thead>
<tbody>
<tr><th scope="row">Learned candidate with contact/gripper supervisor</th><td>Pass</td></tr>
<tr><th scope="row">Identical joint targets, open jaws</th><td>Fail</td></tr>
</tbody>
</table>
</div>
<p class="note">The replay displays saved native physics states. Playback, scrubbing, and view rotation do not run new training or a new physics experiment. A passing recorded scene does not establish generalization to another scene or a physical robot.</p>
<div class="evidence-links">
<a href="replay.html">Open the full recorded replay</a>
<a href="https://huggingface.co/Dvidia/dvidia-training-pilot">Inspect qualification and control receipts β</a>
</div>
<figure class="replay">
<iframe src="replay.html" title="Recorded native simulation qualification: learned candidate and open-jaw control in one held-out scene" loading="lazy" sandbox="allow-scripts allow-downloads" referrerpolicy="no-referrer"></iframe>
<figcaption>Recorded evidence Β· simulation only. Select the candidate or open-jaw control inside the replay, or <a href="replay.html">open it in the full view</a>.</figcaption>
</figure>
</section>
<section class="section" id="install" aria-labelledby="install-title">
<div class="section-heading"><span class="section-number">05</span><h2 id="install-title">Run the tool locally</h2></div>
<p>Install Python 3.12+ and FFmpeg first, then use these macOS/Linux commands. The local studio opens at <a href="http://127.0.0.1:8270">127.0.0.1:8270</a> on your own machine.</p>
<pre><code>git clone https://github.com/Dvidia-Inference/dvidia-training.git
cd dvidia-training
python3.12 -m venv .venv
.venv/bin/python -m pip install .
.venv/bin/dvidia-training-studio --directory runs/studio</code></pre>
<p>To reproduce the synthetic visual path, create a small example, train it, and inspect its export. Each run requires a fresh output directory.</p>
<pre><code>.venv/bin/dvidia-train example --clips 10 --output examples/visual-demo
.venv/bin/dvidia-train run examples/visual-demo --output runs/visual-demo
.venv/bin/dvidia-train inspect runs/visual-demo</code></pre>
<p>The optional native arm branch adds MuJoCo and the capsule tools through <code>.venv/bin/python -m pip install ".[arm]"</code>. Its task contract and closed-loop qualification are separate from visual fitting.</p>
<div class="evidence-links">
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/docs/installation.md">Complete installation guide β</a>
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/docs/installation.md#windows-powershell">Windows PowerShell β</a>
<a href="https://github.com/Dvidia-Inference/dvidia-training/blob/main/docs/hardware.md">Check your machine β</a>
</div>
</section>
<section class="section" id="artifacts" aria-labelledby="artifacts-title">
<div class="section-heading"><span class="section-number">06</span><h2 id="artifacts-title">Inspect the published artifacts</h2></div>
<p>The source, example data, and pilot outputs are available for inspection and local reproduction. Integrity hashes check consistency; they do not authenticate capture history or independently establish measured truth.</p>
<dl class="download-list">
<div><dt><a href="https://huggingface.co/datasets/Dvidia/dvidia-training-examples">Example dataset β</a></dt><dd>Explicitly synthetic visual examples and native simulation evidence, with frozen prepared inputs under <code>visual/prepared</code> and <code>native/prepared</code>. These are software and simulation fixtures, not recorded human skill demonstrations.</dd></div>
<div><dt><a href="https://huggingface.co/Dvidia/dvidia-training-pilot">Pilot artifacts β</a></dt><dd>Visual weights, the learned movement candidate, evaluation reports, and qualification/control evidence. A movement candidate remains bound to its declared adapter and scene qualification.</dd></div>
<div><dt><a href="https://github.com/Dvidia-Inference/dvidia-training">Source repository β</a></dt><dd>The MIT-licensed local training tool, installation guides, data contracts, and reproduction commands.</dd></div>
<div><dt><a href="https://research.dvidia.org/papers/skillspace-footage-training/">Research publication β</a></dt><dd>The pilot methods, observations, and measured limitations in the DVIDIA research notebook.</dd></div>
</dl>
</section>
<section class="section" id="limits" aria-labelledby="limits-title">
<div class="section-heading"><span class="section-number">07</span><h2 id="limits-title">What remains open</h2></div>
<p>The long-term aim is installable robot skills. This research alpha is a smaller, inspectable step toward that aim.</p>
<ul class="limit-list">
<li>Visual prediction error does not establish task success, calibrated geometry, or robot competence.</li>
<li>No human-video action bridge, shoe organization policy, or demonstration sufficiency threshold has been established.</li>
<li>The movement evidence covers one held-out synthetic recording and its exact simulation scene. Independent scene generalization remains unmeasured.</li>
<li>Task phases, contact/gripper logic, and recovery remain authored. Hardware sensing, calibration, actuation, and physical qualification remain separate work.</li>
</ul>
<p class="note">Research alpha Β· no qualified hardware skill Β· no hosted training or uploads.</p>
</section>
<footer class="footer">
<p>DVIDIA Β· Open physical skills Β· MIT licensed</p>
<p><a href="https://research.dvidia.org">Research notebook</a> Β· <a href="https://github.com/Dvidia-Inference/dvidia-training">GitHub</a></p>
</footer>
</article>
</main>
</body>
</html>
|