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<!doctype html><html lang="en"><head><meta charset="utf-8"><meta name="viewport" content="width=device-width,initial-scale=1"><title>Results</title><style>
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</style></head><body><div class="wrap"><nav><a href="index.html">overview</a><a href="method.html">method</a><a href="results.html" aria-current="page">results</a><a href="sensors.html">sensors</a><a href="gallery.html">gallery</a><a href="workbench.html">3D workbench</a></nav>
<h1>Results</h1>
<p>Both reconstructions through <b>one</b> protocol: half the frames in each
group fit a 5-feature least squares, half are scored; pooled ρ, five seeds,
beside a within-group label shuffle. The second number in each cell is ρ minus
that row's own floor — the comparable one — and bold marks the larger. Only
the image→gradient step differs.</p>

<h3>Presses the sensor images whole</h3>
<div class="tablewrap"><table><thead><tr><th>dataset</th><th>n</th><th>LUT<br>ρ / margin</th><th>calibration-free<br>ρ / margin</th><th>shuffle floor</th><th>groups fitted</th></tr></thead><tbody>
<tr><td>GelSight Mini, CNC presses, 0-20 N</td><td>605<br><span class='dim'>of 6,219</span><td><b>0.993<br><span class='dim'>+0.925</span></b></td><td>0.998<br><span class='dim'>+0.909</span></td><td class='dim'>+0.067 / +0.089</td><td class='dim'>6/6</td></tr><tr><td>FoTa cnc_Mini</td><td>604<br><span class='dim'>of 3,351</span><td><b>0.900<br><span class='dim'>+0.940</span></b></td><td>0.938<br><span class='dim'>+0.903</span></td><td class='dim'>-0.040 / +0.035</td><td class='dim'>6/6</td></tr><tr><td>FEATS (MARKER gel)</td><td>2,000<br><span class='dim'>of 16,276</span><td>0.642<br><span class='dim'>+0.553</span></td><td><b>0.706<br><span class='dim'>+0.617</span></b></td><td class='dim'>+0.089 / +0.089</td><td class='dim'>8/8</td></tr><tr><td>Sparsh / Meta, 10 gel pads</td><td>2,000<br><span class='dim'>of 129,389</span><td>0.959<br><span class='dim'>+0.909</span></td><td><b>0.986<br><span class='dim'>+0.956</span></b></td><td class='dim'>+0.050 / +0.030</td><td class='dim'>4/4</td></tr><tr><td>FeelAnyForce<br><span class='bad'>floor-dominated</span></td><td>2,000<br><span class='dim'>of 13,892</span><td>0.939<br><span class='dim'>+0.197</span></td><td><b>0.949<br><span class='dim'>+0.208</span></b></td><td class='dim'>+0.742 / +0.741</td><td class='dim'>13/14</td></tr></tbody></table></div>

<figure><img src="assets/truncation.png" alt="truncated presses">
<figcaption>A press is <b>truncated</b> when its contact core reaches a border:
the indentation continues outside the frame, so the free-boundary solve runs
off the edge with nothing to stop the ramp.
15.9% of them reconstruct deeper than the
4.25&nbsp;mm gel, against
0.0% of whole presses (441 and
59 frames). Their depth is not identifiable from the
image.</figcaption></figure>

<p>Excluding them is what the headline row buys: on GelSight Mini, CNC presses, 0-20 N
calibration-free scores ρ&nbsp;0.998
on whole presses against
0.851 once truncated frames are
mixed in. Three datasets reach the 2,000 this table samples; the two that
cannot have no more presses to give.</p>

<h3>All frames</h3>
<p class="dim">The same protocol without that exclusion.</p>
<div class="tablewrap"><table><thead><tr><th>dataset</th><th>n</th><th>LUT<br>ρ / margin</th><th>calibration-free<br>ρ / margin</th><th>shuffle floor</th><th>groups fitted</th></tr></thead><tbody>
<tr><td>GelSight Mini, CNC presses, 0-20 N</td><td>2,376<br><span class='dim'>of 6,219</span><td>0.707<br><span class='dim'>+0.571</span></td><td><b>0.851<br><span class='dim'>+0.713</span></b></td><td class='dim'>+0.135 / +0.138</td><td class='dim'>6/6</td></tr><tr><td>FoTa cnc_Mini</td><td>2,239<br><span class='dim'>of 3,351</span><td>0.442<br><span class='dim'>+0.416</span></td><td><b>0.482<br><span class='dim'>+0.449</span></b></td><td class='dim'>+0.026 / +0.033</td><td class='dim'>6/6</td></tr><tr><td>FEATS (MARKER gel)</td><td>2,956<br><span class='dim'>of 16,276</span><td><b>0.707<br><span class='dim'>+0.525</span></b></td><td>0.702<br><span class='dim'>+0.506</span></td><td class='dim'>+0.183 / +0.196</td><td class='dim'>8/8</td></tr><tr><td>Sparsh / Meta, 10 gel pads</td><td>3,091<br><span class='dim'>of 129,389</span><td>0.669<br><span class='dim'>+0.629</span></td><td><b>0.698<br><span class='dim'>+0.657</span></b></td><td class='dim'>+0.040 / +0.041</td><td class='dim'>4/4</td></tr><tr><td>FeelAnyForce</td><td>3,378<br><span class='dim'>of 13,892</span><td>0.905<br><span class='dim'>+0.509</span></td><td><b>0.935<br><span class='dim'>+0.531</span></b></td><td class='dim'>+0.396 / +0.404</td><td class='dim'>14/14</td></tr></tbody></table></div>

<p class="dim">The shuffle floor is an absolute ρ — what this protocol scores
with labels permuted inside each group; the margin beside each cell already has
it subtracted. React's production number adds a fitted position gain field and
lives on the <a href="method.html">method</a> page.</p>

<figure><img src="assets/pred_vs_gt.png" alt="predicted vs ground-truth force">
<figcaption>Held-out prediction against ground truth, shared axes per row.
Each panel carries its shuffle floor and the margin over it.</figcaption>
</figure>

<figure><img src="assets/cross_dataset.png" alt="cross-dataset transfer">
<figcaption>Fit on one dataset, predict on every other. Read each cell against
the random-weight baseline under its column: the features are collinear and all
monotone in contact size, so on an easy target almost any direction ranks
correctly.</figcaption></figure>

<p>FoTa cnc_Mini→FEATS, Sparsh→FEATS are ≥99&nbsp;% extrapolation. — there MAE is extrapolation, not prediction.
FeelAnyForce's row goes <i>negative</i>: collinear features let least squares
cancel opposite-sign terms (<a href="method.html">method</a>). Non-negative
weights fix it: off-diagonal ρ
0.574&nbsp;→&nbsp;0.731, negative
cells 3&nbsp;→&nbsp;0 of
20, costing 0.010 on the diagonal.
<b>The deployed estimator is unchanged</b>: on React both agree at
ρ&nbsp;0.989 (1.8&nbsp;% of
frames outside the rig's range), and 15 held-out seeds differ by
+0.002&nbsp;±&nbsp;0.014&nbsp;ρ.</p>

<h2>Which reconstruction for React's force channel?</h2>
<p>React's own calibration objects <b>cannot answer this</b>: calibration-free
scores ρ&nbsp;0.781 against the LUT's
0.763 on 158 held-out presses, but
a paired bootstrap puts the margin at 95%&nbsp;CI
[-0.081,
+0.120] — a coin flip. Nor does the
table: calibration-free leads on raw ρ everywhere, but over each row's own
floor it is ahead on 3 of 5. It ships because it needs no per-sensor lookup
table, not because it measures force better.</p>
<p>The two agree at ρ&nbsp;=&nbsp;0.925 over 2,400 React
frames, mean difference 0.86&nbsp;N. Published to <code>yxma/React</code>: this channel across all 72 sides of 36 episodes (480,080 frames).</p>

<h2>Error analysis</h2>
<p>The ten worst held-out frames reconstruct as well as the five best — same
gradient dipoles, same compact depth, no ramping. The residual is in the
depth→force fit, not in image→depth, so a better reconstruction will not move
them.</p>
<p class='dim'>Each panel: the ten worst held-out frames, with the five best as a control. Relative error is |pred−true| over the dataset's force span.</p><figure><img src="assets/errors_cnc_mini_26.png" alt="cnc_mini_26 errors"><figcaption>GelSight Mini CNC, span 19.50&nbsp;N — median 0.7%, p90 2.0%, worst 11.6%.</figcaption></figure><figure><img src="assets/errors_cnc.png" alt="cnc errors"><figcaption>FoTa cnc_Mini, span 4.01&nbsp;N — median 4.9%, p90 15.1%, worst 35.0%.</figcaption></figure><figure><img src="assets/errors_feats.png" alt="feats errors"><figcaption>FEATS (marker), span 59.86&nbsp;N — median 3.1%, p90 10.3%, worst 39.3%.</figcaption></figure><figure><img src="assets/errors_sparsh.png" alt="sparsh errors"><figcaption>Sparsh / Meta, span 1.07&nbsp;N — median 1.9%, p90 6.2%, worst 48.2%.</figcaption></figure><figure><img src="assets/errors_faf.png" alt="faf errors"><figcaption>FeelAnyForce, span 17.44&nbsp;N — median 1.7%, p90 9.7%, worst 41.3%.</figcaption></figure>
</div></body></html>