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<!DOCTYPE html>
<html lang="en"><head><meta charset="utf-8">
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<title>Results — Force Estimation on GelSight Mini</title>
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<header>
<div class="kicker">React force recovery · results</div>
<h1>Three estimators × three ground-truth datasets</h1>
<p class="sub">Every method evaluated on every dataset with force-sensor labels,
predicted vs ground truth per dataset — so dataset quality is controlled within
each row, and differences between panels are differences between methods.</p>
<a class="pill" href="method.html">↖ how the method is designed</a>
<a class="pill" href="index.html">overview</a>
<a class="pill" href="gallery.html">gallery</a>
<a class="pill" href="results_zh.html">中文</a>
</header>

<h2>The matrix (Spearman ρ, predicted vs F/T ground truth)</h2>
<div class="card"><table class="matrix">
<tr><th>dataset (gel type)</th><th>Ours — physics,<br>0 training frames</th>
<th>FEATS U-net<br>(trained: marker gel)</th>
<th>FeelAnyForce<br>(trained: markerless)</th></tr>
<tr><td>FEATS val (marker)</td><td>0.77</td>
<td class="best">0.96 · in-domain</td><td class="dead">0.43</td></tr>
<tr><td>FoTa cnc_Mini (markerless)</td><td>0.94 (in view)</td>
<td class="dead">0.07</td><td class="best">0.83</td></tr>
<tr><td>GlowTact (markerless)</td><td>0.98</td>
<td class="dead">0.04</td><td class="best">0.90</td></tr>
</table>
<p>The pattern is the finding: <b>each network dominates its own gel domain and
collapses outside it</b> (FEATS 0.96 → 0.04–0.07; FeelAnyForce 0.90 → 0.43),
while <b>the physics pipeline is the only estimator that works everywhere</b>
(0.74–0.99) — it never sees training data, so it has no domain to leave.</p>
</div>

<h2>FEATS dataset — marker-dot gel</h2>
<div class="card"><img src="assets/results_feats.png" alt="FEATS dataset panels">
<p class="footnote">In-domain, the FEATS U-net is excellent (ρ=0.96) — the
negative results elsewhere are domain effects, not a weak model. FeelAnyForce,
markerless-trained, degrades on the dotted gel (0.43): the same knife cuts both
ways.</p></div>

<h2>FoTa cnc_Mini — markerless gel</h2>
<div class="card"><img src="assets/results_cnc.png" alt="cnc_Mini panels">
<p class="footnote">Hard conditions: only 4 contact-free frames, 62% of presses
near the pad border — the press grid is larger than the field of view (see
<a href="debug_pipeline.html">pipeline debug</a>). Strictly in view our ρ is
0.94 (MAE 0.26 N); FeelAnyForce reaches 0.92 non-edge.</p></div>

<h2>GlowTact — markerless gel, cleaned</h2>
<div class="card"><img src="assets/results_glowtact.png" alt="GlowTact panels">
<p class="footnote">Friendliest ground truth (centred presses, 10 free frames,
0–20 N). Per-indenter, calibrated within each family under the physical scope
(contact fully in view, gel not bottomed out), ρ is 0.975–0.992 across all six
indenters with MAE ≤ 0.73 N. Caveat: that calibration is refit per family, so
it measures rank agreement within a group, not a transferable absolute-newton
scale — see <a href="method.html">per-dataset calibration</a>.</p></div>

<h2>Sparsh (Meta) — a fourth dataset, and where the method breaks</h2>
<div class="card"><img src="assets/results_sparsh.png" alt="Sparsh results">
<p class="footnote">10 gel pads (6 sphere, 2 flat, 2 sharp), force in newtons.
Our GlowTact table applied to this foreign sensor reaches &rho;=0.878 on
in-view frames. Rebuilding the table from <b>Sparsh's own sphere presses</b>
— 708 frames, radius fitted at R=2.44 mm from a&sup2;=d(2R&minus;d) — takes it
to <b>&rho;=0.968, MAE 0.042 N</b>, against a labels-shuffled-within-pad
control of 0.23. Fitting on one pad and applying it <b>unchanged</b> to another
costs nothing measurable (0.96&ndash;0.98 everywhere): one table, six gel pads.</p>
<img src="assets/sparsh_dome.png" alt="dome before and after">
<p class="footnote">Why the table matters more than the fit: with the wrong
sensor's table a sphere press integrates to a <b>bilobed shape with a central
dip</b>; with the self-calibrated table it is a single dome matching the
analytic spherical cap (residual RMS 0.179 &rarr; <b>0.0545 mm</b>). Measured
before any integration, the LUT gradient sits <b>93.3&deg;</b> from the analytic
sphere gradient — chance is 90&deg; — and self-calibration brings it to
<b>4.5&deg;</b> (within 30&deg;: 15% &rarr; 99%). Reverse control: the Sparsh
table fails on GlowTact frames too, so this is a per-sensor property, not a
bad table.</p>
<p class="footnote"><b>What this costs and where it still fails.</b> The price
is one set of sphere presses with logged depth on the target sensor: this is
<b>calibrate once per sensor</b>, not zero-shot. Rank order transfers across
indenter shapes but <i>absolute newtons do not</i> — a sphere-fitted model
applied to a flat punch degrades to MAE 0.37&ndash;0.40 N, and it got
<i>worse</i> with the correct table, because true geometry widens the real
feature-scale gap between a sphere and a punch. The sharp indenter is
unsupported (in-view &rho; 0.58). Shear stays out of reach by construction —
the top shear decile keeps 1.6&times; the residual whichever table is used.
Frames are restricted to a visible contact disc: 36% of presses show none and
11% are clipped, and the clipped subset carries the <i>highest</i> median force
yet scores worse, so this is visibility, not force-range filtering. Three
dataset defects had to be fixed first: flat/sharp trajectories ship 5 more
frame indices than forces (silently drifting the labels, &rho;&asymp;0 until
paired within each trajectory), sharp/batch_2 is stored BGR while the other
nine are RGB, and flat/batch_2 ships only 3 of 4 image files.</p></div>

<h2>React — no ground truth, so: do independent methods agree?</h2>
<div class="card"><img src="assets/results_react.png" alt="React agreement">
<p class="footnote">On the dataset we actually care about, the two surviving
estimators — physics (zero training) and FeelAnyForce (200K frames) — agree at
ρ=0.91, and FeelAnyForce reads ≈0 N on every frame the physics pipeline calls
contact-free. Neither can copy the other's mistakes.</p></div>

<h2>Takeaway</h2>
<div class="card"><p style="margin-top:0"><b>For labelling React</b> (markerless
Mini): FeelAnyForce as the primary labeller, the physics pipeline as an
independent audit, disagreement rows flagged. <b>For any new gel or sensor</b>
where no trained model matches the domain: the physics pipeline is the only
option that works out of the box — and its FEATS-dataset score (0.74) shows
what it does on a domain nobody tuned it for.</p></div>

<p><a href="debug_pipeline.html"><b>Pipeline debug page</b></a>: raw
image &rarr; force step by step on all three datasets, and the cnc
field-of-view ablation (in-view &rho;=0.94).</p>

<footer>React force recovery · <a href="index.html">overview</a> ·
<a href="method.html">method design</a> · <a href="gallery.html">gallery</a> ·
data: FEATS (2411.03315) · FoTa/T3 (2406.13640) · GlowTact
(dacongming666/GlowTact_Datasets) · FeelAnyForce (2410.02048)</footer>
</div></body></html>