| # Cable representation analysis — ACT encoder, DG-5F / UR5e cable sorting |
|
|
| What the ACT encoder's internal representation holds about the cable being |
| handled. All figures come from a forward hook on `policy.model.encoder`: the |
| transformer tokens are mean-pooled into a single 512-d vector per frame. |
|
|
| **Every split in this directory is episode-level.** Frames inside one episode |
| are near-duplicates, so a frame-level split leaks the answer and inflates every |
| number below. |
|
|
| ## Model under analysis |
|
|
| | | | |
| |---|---| |
| | policy | ACT, action chunk 60 | |
| | training | 300k steps on `Kaz55/dg5f_ur5e_bluev2_cable170_330_270ep` (270 episodes) | |
| | cameras | 2× RealSense + 2× GelSight | |
| | excluded | `observation.velocity`, `observation.effort` | |
|
|
| Cable naming: **1.70 sq → 1.7 mm**, **blue → 2.6 mm**, **3.30 sq → 4.5 mm** |
| (mm = outer diameter; sq = conductor cross-section). |
|
|
| ## Headline results |
|
|
| **The encoder knows which cable it is holding.** |
|
|
| ``` |
| full 512-d linear probe, 3 cables 97.9% (chance 33.3%) |
| full 512-d linear probe, 4 cable sizes 94.0% (chance 25.0%) |
| ``` |
|
|
| **It encodes thickness as a quantity, not just as a class label.** Ridge |
| regression onto physical diameter, fitted on training episodes only — the |
| ordering is never supplied, yet 2.6 mm lands between 1.7 and 4.5: |
|
|
| ``` |
| held-out R^2 = 0.88 |
| true 1.7 mm -> 1.81 +- 0.33 mm |
| true 2.6 mm -> 2.66 +- 0.39 mm |
| true 4.5 mm -> 4.33 +- 0.43 mm |
| ``` |
|
|
| **But the discrimination is visual, not tactile.** Splitting each episode at |
| contact onset (detected per episode from finger torque) gives almost the same |
| accuracy before the GelSight has touched anything as after: |
|
|
| ``` |
| probe accuracy diameter R^2 |
| pre-contact 94.9% 0.72 |
| post-contact 95.6% 0.83 |
| ``` |
|
|
| This reconciles the training-loss result seen across 4+ datasets: **removing |
| GelSight entirely leaves training loss unchanged**, and GelSight resolution from |
| 192 tokens down to 0 makes no difference. The policy separates cables by sight. |
|
|
| PCA in each phase, probe accuracy on exactly the axes drawn: |
|
|
| ``` |
| PC1/PC2 PC1-PC3 variance PC1 / PC2 / PC3 |
| pre-contact 46.7% 77.2% 18.9% / 9.2% / 5.8% |
| post-contact 64.9% 82.8% 9.7% / 7.0% / 6.1% |
| ``` |
|
|
| PC1/PC2 is dominated by arm pose, which is why the unsupervised 2-D view looks |
| messier than the probe numbers suggest. The cable-identity structure lives on |
| later components (PC3/PC4) and in the 3-D view. |
|
|
| ## Four cable sizes — does an intermediate size land in between? |
|
|
| `4sizes/`. Adds **3.00 sq**, an intermediate cross-section, from |
| `Kaz55/dg5f_ur5e_cable3sizes_180ep`. 900 frames per size (60 episodes × 15 |
| frames), 3600 total. |
|
|
| **Yes — 3.00 sq sits between blue and 3.30 sq.** On the regressed size axis: |
|
|
| ``` |
| size axis held-out R^2 = 0.86 |
| 1.70 sq true 1.70 -> 1.83 +- 0.22 |
| blue (2.6 mm) true 2.30 -> 2.36 +- 0.22 |
| 3.00 sq true 3.00 -> 2.94 +- 0.22 |
| 3.30 sq true 3.30 -> 3.21 +- 0.20 |
| ``` |
|
|
| and in raw 512-d centroid distances, where blue↔3.30 is the largest of the three |
| — exactly what it must be if 3.00 lies between them: |
|
|
| ``` |
| blue <-> 3.00 4.59 |
| 3.00 <-> 3.30 3.33 |
| blue <-> 3.30 5.27 <- largest |
| ``` |
|
|
| **Caveat.** 3.00 sq comes from a different dataset, so session-level confounds |
| (lighting, gel condition, recording day) travel with that label. The ordering |
| result is what guards against over-reading it: a session artefact has no reason |
| to land *between* two other sizes — it would sit off to one side. |
|
|
| `blue` was entered as 2.30 sq for the ordering test; the ordering itself does not |
| depend on that value. |
|
|
| ## Directory map |
|
|
| | path | contents | |
| |---|---| |
| | `4sizes/` | t-SNE and size axis over 4 cable sizes, `results_4sizes.txt` | |
| | `contact/` | pre/post-contact PCA (2-D, 3-D, rotating GIFs), phase analysis, `results_contact.txt` | |
| | `diameter/` | diameter-ordered clusters, `results_diameter.txt` | |
| | `pca2d/` | PC1/PC2 views, informative-component views | |
| | `pca3d/` | PC1-PC3, rotating GIF | |
| | `tsne/` | t-SNE over the 3 original cables | |
| | `pptx/` | `cable_representation.pptx` — the figures as PowerPoint slides | |
| | `cache/` | extracted encoder features (`.npz`) — every figure replots from these without a GPU | |
| | `scripts/` | extraction (`collect_*.py`) and plotting (`plot_*.py`) | |
| | `results_probe.txt` | full-512-d probe accuracies | |
|
|
| ## Reproducing |
|
|
| Replot from the cached features (no GPU, no model download): |
|
|
| ```bash |
| python scripts/plot_4sizes_tsne.py --cache cache/cache_4sizes.npz --outdir 4sizes/ |
| python scripts/plot_contact_pca.py --cache cache/cache_cable_contact.npz --out contact/cable_contact_pca.png |
| python scripts/summarize_from_cache.py --cache cache/cache_cable_feats.npz |
| ``` |
|
|
| Re-extract features from the trained policy (needs the checkpoint and a GPU): |
|
|
| ```bash |
| python scripts/collect_4sizes.py --policy-path <checkpoint> |
| python scripts/collect_contact_feats.py --policy-path <checkpoint> |
| ``` |
|
|
