| |
| """Build a PowerPoint deck from the cable-representation figures. |
| |
| Slides carry the figures only; the wording that would otherwise clutter the plot |
| goes into the speaker notes, so the images stay usable as-is in a paper too. |
| """ |
| import numpy as np |
| from pptx import Presentation |
| from pptx.util import Inches, Pt |
|
|
| FIGDIR = "outputs/pca" |
| OUT = f"{FIGDIR}/cable_representation.pptx" |
|
|
| SLIDES = [ |
| dict( |
| title="Cable clusters in the learned representation", |
| image=f"{FIGDIR}/cable_diameter_clusters.png", |
| notes=( |
| "Left: unsupervised PCA of the ACT encoder features (512-d), plotted on PC4 vs PC3 " |
| "-- the two components that carry cable identity. PC1/PC2 are dominated by arm pose " |
| "and grasp phase, so the default projection hides the cable entirely.\n\n" |
| "Right: a diameter axis. Ridge regression maps the same encoder features onto physical " |
| "cable diameter, fitted on training episodes only. 2.6 mm lands between 1.7 and 4.5 mm " |
| "even though the model was never given the ordering -- it only ever saw three task " |
| "strings. Held-out R^2 = 0.88.\n\n" |
| " true 1.7 mm -> predicted 1.81 +- 0.33 mm\n" |
| " true 2.6 mm -> predicted 2.66 +- 0.39 mm\n" |
| " true 4.5 mm -> predicted 4.33 +- 0.43 mm\n\n" |
| "This means thickness is encoded as a quantity, not as three arbitrary labels: a pure " |
| "3-way classifier would place the clusters anywhere, not in metric order.\n\n" |
| "Method: 30 episodes per cable, 25 frames per episode (2250 frames). Split is by " |
| "EPISODE, not by frame -- frames inside one episode are near-duplicates, so a " |
| "frame-level split leaks the answer. Only held-out episodes (675 frames) are plotted. " |
| "Model: act-bluev2-cable170-330-270ep-ac60, 300k checkpoint." |
| ), |
| ), |
| dict( |
| title="Why PC1/PC2 hides it", |
| image=f"{FIGDIR}/pca_cable_informative_300k.png", |
| notes=( |
| "Same PCA, three views. Left is the default PC1 vs PC2: the three cables are fully " |
| "mixed (separability 0.13 and 0.00). Middle is PC4 vs PC3 from the identical " |
| "decomposition -- the clusters are there all along. Right shows separability per " |
| "component: the cable signal sits almost entirely in PC4 (ratio 1.53).\n\n" |
| "PC4 accounts for only 5.0% of variance while PC1 accounts for 11.2%. PCA orders axes " |
| "by variance, not by class separation, so a larger but cable-irrelevant factor " |
| "outranks it. Labels were used only to choose which existing axis to look along; the " |
| "axes themselves are unsupervised." |
| ), |
| ), |
| dict( |
| title="The information is really there", |
| image=f"{FIGDIR}/cable_repr_analysis_300k.png", |
| notes=( |
| "A linear probe settles it independently of any visualisation. Logistic regression on " |
| "the encoder features predicts cable identity with 97.9% accuracy on held-out episodes " |
| "(chance 33.3%). Raw GelSight pixels reach 95.9%.\n\n" |
| "Left and middle are LDA projections -- supervised, so they are fitted on training " |
| "episodes and drawn on held-out ones. Fitting and plotting the same data would " |
| "manufacture clusters that do not generalise.\n\n" |
| "Caveat: the model sees four cameras (RealSense x2 + GelSight x2), so this number does " |
| "not attribute the signal to touch. Comparing against act-bluev2-gs0-ac60, trained " |
| "without GelSight, would separate visual from tactile contribution. Note that across " |
| "four datasets, removing GelSight left training loss unchanged." |
| ), |
| ), |
| ] |
|
|
|
|
| def main(): |
| prs = Presentation() |
| prs.slide_width, prs.slide_height = Inches(13.333), Inches(7.5) |
| blank = prs.slide_layouts[6] |
|
|
| for s in SLIDES: |
| slide = prs.slides.add_slide(blank) |
|
|
| tb = slide.shapes.add_textbox(Inches(0.6), Inches(0.35), Inches(12.1), Inches(0.7)) |
| p = tb.text_frame.paragraphs[0] |
| p.text = s["title"] |
| p.font.size, p.font.bold = Pt(26), True |
|
|
| |
| from PIL import Image |
| w, h = Image.open(s["image"]).size |
| avail_w, avail_h = Inches(12.1), Inches(5.9) |
| scale = min(avail_w / w, avail_h / h) |
| iw, ih = int(w * scale), int(h * scale) |
| slide.shapes.add_picture(s["image"], |
| int((prs.slide_width - iw) / 2), Inches(1.25), |
| width=iw, height=ih) |
|
|
| slide.notes_slide.notes_text_frame.text = s["notes"] |
|
|
| prs.save(OUT) |
| print(f"wrote {OUT}") |
| print(f" {len(SLIDES)} slides, figures only; explanations in the speaker notes") |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
