README: add 4 new subsets (gelslam, tactile_tracking, real_tactile_mnist, feelanyforce)

README.md

@@ -46,13 +46,33 @@ configs:
         path: feats/test_diff_sensor_old_gel-*.parquet
       - split: test_unknown_indenters
         path: feats/test_unknown_indenters-*.parquet
+  - config_name: gelslam
+    data_files:
+      - split: train
+        path: gelslam/train-*.parquet
+      - split: recon
+        path: gelslam/recon-*.parquet
+  - config_name: tactile_tracking
+    data_files:
+      - split: train
+        path: tactile_tracking/train-*.parquet
+  - config_name: real_tactile_mnist
+    data_files:
+      - split: train
+        path: real_tactile_mnist/train-*.parquet
+      - split: test
+        path: real_tactile_mnist/test-*.parquet
+  - config_name: feelanyforce
+    data_files:
+      - split: train
+        path: feelanyforce/train-*.parquet
 ---
 
 # GelSight Mini Pretrain
 
 ![overview](assets/combined_overview.png)
 
-A unified, parquet-native collection of **600K+ raw [GelSight Mini](https://www.gelsight.com/gelsightmini/) tactile RGB frames** for self-supervised representation learning. Four well-known public datasets are aggregated under one schema, with a clean **markered vs markerless** split so models that learn from one gel variant aren't confused by the other.
+A unified, parquet-native collection of **~865K raw [GelSight Mini](https://www.gelsight.com/gelsightmini/) tactile RGB frames** for self-supervised representation learning. **Eight** public datasets are aggregated under one schema, with a clean **markered vs markerless** split so models that learn from one gel variant aren't confused by the other.
 
 ## TL;DR
 
@@ -110,7 +130,16 @@ FEATS' raw frames include captures where the indenter was hovering off the gel (
 ![feats](assets/samples_100_feats.png)
 <sub>*FEATS samples after empty-frame removal — every frame now shows a real contact.*</sub>
 
-Final result: **~600K frames** across 4 subsets, **~22 GB on disk**, one schema, one image codec, one `markered` flag.
+**5. Tier-2 expansion: adding four more markerless Mini sources**
+After the initial release a second pipeline pass added GelSLAM, TactileTracking, Real Tactile MNIST, and FeelAnyForce — four more public CC-BY/MIT-licensed GelSight Mini datasets. Each was processed through a shared backbone:
+
+- **Adaptive subsampling** per source, capped at 200K kept frames so no single source dominates.
+- **Validity filter** for video sources (GelSLAM, TactileTracking): a per-capture baseline is computed from the median of the first 10 frames; each subsequent frame is kept only if its central deformation from baseline exceeds ~4 grey levels. Up to 3% of kept frames are allowed below this threshold (variance).
+- **Filter disabled** for already-curated sources (FeelAnyForce indentation stills, Real Tactile MNIST middle-of-touch frames) where every frame is by construction in-contact.
+- **Perceptual-hash dedupe** within each capture (Hamming ≤ 4 on 8×8 DCT low-frequency hash) to drop near-identical adjacent frames from slow indentation videos.
+- **Per-source frame budget** per dataset (see "Composition" above for kept counts; raw → kept summary in [§How this dataset was built](#how-this-dataset-was-built)).
+
+Final result: **~865K frames** across 8 subsets, **~24 GB on disk**, one schema, one image codec, one `markered` flag.
 
 ## Composition
 
@@ -120,9 +149,15 @@ Final result: **~600K frames** across 4 subsets, **~22 GB on disk**, one schema,
 | `fota_unlabeled`  | FoTA — same captures, video frames      | **516,523** (mixed¹)| mixed¹ | object name only            |
 | `threedcal`       | py3DCal sphere indentation grid          | **36,270**| markerless | probe x, y, penetration depth (mm)       |
 | `feats`           | FEATS indentation with force grids       | **16,711**| **markered** (two gel variants — see below) | indenter shape/size + contact forces |
+| `gelslam`         | GelSLAM tactile SLAM tracking + reconstruction | **60,982** (28K tracking + 33K reconstruction) | markerless | episode + object name |
+| `tactile_tracking`| TactileTracking (NormalFlow) 6DoF pose tracking | **1,143** | markerless | object + trial id |
+| `real_tactile_mnist` | Real Tactile MNIST 3D-printed digit touches | **153,600** (128K train + 25.6K test) | markerless | digit class (0–9) + round id |
+| `feelanyforce`    | FeelAnyForce force-controlled indentations | **50,997** | markerless² | object name |
 
 ¹ FoTA used **different gels on the two gripper fingers** for many of its captures. Approximately 36 of 124 captures use a markered gel on the right finger and a markerless gel on the left; the remaining 88 captures use markerless gels on both. The per-row `markered` column was set by averaging ~50 frames per capture and counting visible dark dots in the mean image (threshold ≥10 dots). Use it to filter:
 
+² FeelAnyForce is colloquially described as a "markered Mini" dataset in some references, but visual inspection of the released tactile images confirms the gel surface is **smooth (markerless)**. The `markered` column reflects what is actually observed in the data.
+
 ```python
 ds = load_dataset("yxma/gelsight-mini-pretrain", "fota_labeled", split="train")
 markerless = ds.filter(lambda r: not r["markered"])
@@ -167,6 +202,30 @@ Six indenter shapes (sphere, cuboid, cylinder, pyramid, cross, plus one "unknown
 ### `fota_unlabeled` &nbsp;·&nbsp; 516,523 frames
 Visually identical to `fota_labeled` — same sensor, same objects, same captures — except these are the dense video frames between the labelled stills, with object name only (no pose). This is the bulk of the data and the primary target for self-supervised pretraining.
 
+### `gelslam` &nbsp;·&nbsp; 60,982 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-episode 6DoF
+The GelSLAM dataset of [Huang et al. 2025](https://arxiv.org/abs/2508.15990) — markerless GelSight Mini videos of an object being pressed into the gel and slid around. Two splits:
+- `train` (27,763): the **tracking dataset** — 140 short episodes across 20 objects, each ~21 s long. Suitable for SLAM, pose-from-touch, and continuous pretraining.
+- `recon` (33,219): the **reconstruction dataset** — 15 longer videos (1–30 min) of tactile scans across 15 objects (food, rocks, tool handles).
+
+Per-frame `frame_idx` and per-row `episode` columns are populated. After empty-frame and dedupe filtering, **kept 21% of raw frames** (the majority of GelSLAM frames are pre/post-contact approach motion, which the validity filter removes).
+
+![gelslam](assets/samples_100_gelslam.png)
+
+### `tactile_tracking` &nbsp;·&nbsp; 1,143 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-trial 6DoF
+The TactileTracking benchmark from the [NormalFlow paper](https://github.com/rpl-cmu/normalflow) (Huang et al. 2024) — 84 tracking trials across 12 objects. Aggressively filtered (empty-frame + perceptual-hash dedupe) because the trials are short and most consecutive frames are near-identical. Kept rate after filtering: 15%.
+
+![tactile_tracking](assets/samples_100_tactile_tracking.png)
+
+### `real_tactile_mnist` &nbsp;·&nbsp; 153,600 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +digit class
+From the [Real Tactile MNIST benchmark](https://arxiv.org/abs/2506.06361) (Schneider et al. 2025) — **600 3D-printed MNIST digits**, each touched 256 times by a robot arm, giving 153,600 touches in total. The upstream release ships each touch as a short video clip; here we keep **one middle-frame per touch video** (near peak contact). The `digit_class` column gives the digit 0–9; the `episode` column gives the print id (which of the 600 physical digits was touched).
+
+![real_tactile_mnist](assets/samples_100_real_tactile_mnist.png)
+
+### `feelanyforce` &nbsp;·&nbsp; 50,997 frames &nbsp;·&nbsp; markerless &nbsp;·&nbsp; +per-indentation object
+The [FeelAnyForce dataset](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) (Sharei et al. 2024) of robotically-controlled indentations against 42 unique objects (cylinders, cubes, spheres, fruits, household items). Aggressively dedupe-filtered (50% of raw frames dropped) because each indentation is a slow press-and-hold, so adjacent frames are visually near-identical. The schema retains the `obj_name` and `episode` columns; the upstream force labels (in `TacForce_train_set.csv` etc.) are not currently joined in — see the [upstream release](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) for forces.
+
+![feelanyforce](assets/samples_100_feelanyforce.png)
+
 ## Useful statistics
 
 ### `fota_labeled` — pose coverage
@@ -266,6 +325,20 @@ upstream sources if you use the data:
   [Zenodo 18462608](https://zenodo.org/records/18462608) · *CC-BY-4.0*.
 - **FEATS** — Helmut (2025).
   [HF dataset](https://huggingface.co/datasets/erikhelmut/FEATS) · *MIT License*.
+- **GelSLAM** — Huang et al., 2025.
+  [HF dataset](https://huggingface.co/datasets/joehjhuang/GelSLAM_dataset),
+  [GitHub](https://github.com/rpl-cmu/gelslam),
+  [arXiv:2508.15990](https://arxiv.org/abs/2508.15990) · *MIT License*.
+- **TactileTracking / NormalFlow** — Huang, Kaess, Yuan (2024).
+  [HF dataset](https://huggingface.co/datasets/joehjhuang/TactileTracking),
+  [GitHub](https://github.com/rpl-cmu/normalflow),
+  IEEE RA-L 2024 · *MIT License*.
+- **Real Tactile MNIST** — Schneider et al., 2025.
+  [HF dataset family](https://huggingface.co/TimSchneider42),
+  [GitHub](https://github.com/TimSchneider42/tactile-mnist),
+  [arXiv:2506.06361](https://arxiv.org/abs/2506.06361) · *CC-BY-2.0*.
+- **FeelAnyForce** — Sharei et al., 2024.
+  [HF dataset](https://huggingface.co/datasets/amirsh1376/FeelAnyForce) · *CC-BY-4.0*.
 
 Conversion details:
 - All images are re-encoded to JPEG at quality 92. Original PNGs in