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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>0.74</td> | |
| <td class="best">0.96 · in-domain</td><td class="dead">0.43</td></tr> | |
| <tr><td>FoTa cnc_Mini (markerless)</td><td>0.43 (0.65 non-edge)</td> | |
| <td class="dead">0.07</td><td class="best">0.83</td></tr> | |
| <tr><td>GlowTact (markerless)</td><td>0.63</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> | |
| (0.43–0.74) — 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. Our edge-filtered ρ is 0.65; 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-family our ρ reaches 0.93; the pooled gap to FeelAnyForce is | |
| mostly object-dependent volume→force gain, which a single physical scale | |
| cannot capture but 200K supervised frames can.</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 → force step by step on all three datasets, and the cnc | |
| field-of-view ablation (in-view ρ=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> | |
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