README: add 2 sim subsets, domain column, real/sim breakdown, 1.16M total

README.md

@@ -66,13 +66,29 @@ configs:
     data_files:
       - split: train
         path: feelanyforce/train-*.parquet
+  - config_name: sim_tactile_mnist
+    data_files:
+      - split: train
+        path: sim_tactile_mnist/train-*.parquet
+  - config_name: sim_starstruck
+    data_files:
+      - split: train
+        path: sim_starstruck/train-*.parquet
+      - split: test
+        path: sim_starstruck/test-*.parquet
 ---
 
 # GelSight Mini Pretrain
 
 ![overview](assets/combined_overview.png)
 
-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.
+A unified, parquet-native collection of **~1.16 M [GelSight Mini](https://www.gelsight.com/gelsightmini/) tactile RGB frames** for self-supervised representation learning. **Ten** public datasets are aggregated under one schema:
+- **758 K real-world frames** from 8 captures (FoTA, 3DCal, FEATS, GelSLAM, TactileTracking, Real Tactile MNIST, FeelAnyForce)
+- **400 K simulated frames** from 2 Mini-calibrated Taxim renders (Sim Tactile MNIST, Sim Starstruck)
+
+Every row carries a `domain` column (`"real"` or `"sim"`) and a `markered` flag (gel has tracking dots?) so you can mix or filter freely.
+
+![summary pies](assets/summary_pies.png)
 
 ➡️  **For a full per-subset breakdown** (intro, paper, license, processing recipe, sample grids, stats) see **[SOURCES.md](SOURCES.md)**.
 
@@ -183,10 +199,12 @@ Real Tactile MNIST · digit-class balance (used as a sanity check that the upstr
 | `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 |
+| `gelslam`         | GelSLAM tactile SLAM tracking + reconstruction | **89,612** (28K tracking + 61K reconstruction) | markerless | episode + object name |
+| `tactile_tracking`| TactileTracking (NormalFlow) 6DoF pose tracking | **1,605** | markerless | object + trial id |
+| `real_tactile_mnist` | Real Tactile MNIST 3D-printed digit touches | **16,961** (14K train + 2.8K test) | markerless | digit class (0–9) + round id |
 | `feelanyforce`    | FeelAnyForce force-controlled indentations | **50,997** | markerless² | object name |
+| `sim_tactile_mnist` | **SIM** · Taxim-rendered Mini imagery of digit touches | **200,000** | markerless | digit class + episode |
+| `sim_starstruck`  | **SIM** · Taxim-rendered Mini imagery of star objects | **200,000** | markerless | episode |
 
 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:
 
@@ -333,6 +351,33 @@ Every row, regardless of source, has the same columns. Optional fields are `null
 | `f_x`, `f_y`, `f_z` | float32 | (FEATS) total force on probe (N) |
 | `grid_z_max`, `grid_z_mean` | float32 | (FEATS) summary of per-pixel depth grid |
 | `gel_variant`    | string  | (FEATS only) `"black_dot"` (standard markered gel) or `"different"` (the second sensor / different gel used in `test_diff_sensor_new_gel`) |
+| `domain`         | string  | `"real"` for real-world captures or `"sim"` for Taxim-rendered Mini imagery |
+
+## Real vs simulated data
+
+Every row carries a `domain` column. The 758 K real-world frames span 8
+upstream datasets capturing physical robot–object contact on a real
+GelSight Mini sensor; the 400 K simulated frames come from the
+**Mini-calibrated Taxim renderer** of Schneider et al.
+
+```python
+# Real-only markerless pool (largest pretraining target)
+real_markerless = concatenate_datasets([
+    load_dataset("yxma/gelsight-mini-pretrain", c, split="train"
+                ).filter(lambda r: r["domain"] == "real" and not r["markered"])
+    for c in ["fota_unlabeled", "threedcal", "gelslam", "tactile_tracking",
+              "real_tactile_mnist", "feelanyforce"]
+])
+
+# Sim pool — useful for sim-to-real transfer or as augmentation
+sim_pool = concatenate_datasets([
+    load_dataset("yxma/gelsight-mini-pretrain", c, split="train")
+    for c in ["sim_tactile_mnist", "sim_starstruck"]
+])
+```
+
+The two sim subsets are from the **same authors as `real_tactile_mnist`**
+(Schneider et al. 2025) and use the same Mini sensor model.
 
 ## Recommended uses
 
@@ -373,6 +418,13 @@ upstream sources if you use the data:
   [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*.
+- **Sim Tactile MNIST / Sim Starstruck** — Schneider et al., 2025 (same authors as Real Tactile MNIST).
+  [`tactile-mnist-touch-syn-single-t32-320x240`](https://huggingface.co/datasets/TimSchneider42/tactile-mnist-touch-syn-single-t32-320x240),
+  [`tactile-mnist-touch-starstruck-syn-single-t32-320x240`](https://huggingface.co/datasets/TimSchneider42/tactile-mnist-touch-starstruck-syn-single-t32-320x240).
+  Mini-calibrated Taxim renderer. *CC-BY-2.0*.
+- **Taxim** simulator (used by the above sim sources) — Si & Yuan, 2022.
+  [GitHub](https://github.com/Robo-Touch/Taxim),
+  [arXiv:2109.04027](https://arxiv.org/abs/2109.04027).
 
 Conversion details:
 - All images are re-encoded to JPEG at quality 92. Original PNGs in