--- license: cc-by-4.0 task_categories: - image-classification - feature-extraction - image-to-image tags: - tactile - gelsight - gelsight-mini - robotics - tactile-sensing - pretraining - self-supervised size_categories: - 100K*A 3-row overview across the three currently-released subsets. Each row is a different upstream dataset; every frame is a real GelSight Mini capture.* **1. Source verification — "is this really GelSight Mini?"** Each upstream release was checked against the Mini's known native modes (640×480 RGB, occasionally 320×240) and the dotted/dotless gel variants documented by GelSight Inc. We cross-referenced every dataset's `metadata.json`, paper, and folder naming convention to confirm the sensor model before inclusion. FAF is held back until licensing is clarified. **2. Format unification — one schema, one image codec** Upstream layouts are wildly different: FoTA ships **WebDataset tar shards**, py3DCal ships **loose PNGs in pose-named folders**, FEATS ships **`.npy` pickled dicts** containing the image plus a 32×24 depth grid plus forces. Each was decoded to a `numpy` array, re-encoded as **JPEG quality 92** for fair file-size comparison, and packed into the [unified schema](#unified-schema) below as a parquet row. Shards target ~2 GB each so they stream efficiently from HF Hub. **3. Marker detection — fixing FoTA's mixed-gel surprise** FoTA does **not** ship a markered/markerless label, but on visual inspection the captures are clearly mixed (some have ~80 visible tracking dots, others are smooth). We classify automatically: for each FoTA *capture* (one continuous press of an object), average ~50 evenly-spaced frames into a single mean image, then run dark-blob detection on it. Markered gels yield ≥10 well-sized dark dots in the mean; markerless gels yield <10 scattered noise blobs. Result: **36 of 124 captures are markered (all on the right finger), 88 are markerless**. The per-row `markered` column reflects this. | Markered captures (top half) | Markerless captures (bottom half) | |:---:|:---:| | ![fota markered](assets/samples_40_fota_labeled_markered.png) | ![fota markerless](assets/samples_40_fota_labeled_markerless.png) | **4. Empty-frame removal — cleaning FEATS** FEATS' raw frames include captures where the indenter was hovering off the gel (no contact at all). These would teach a pretraining model nothing useful. We filtered them with `|f_z| < 0.5 N` (using FEATS's force-sensor ground truth), removing **5,302 frames** out of 22,013. We also added a `gel_variant` column distinguishing the two physical sensor setups used in FEATS (`black_dot` for the main markered gel, `different` for the second sensor used in `test_diff_sensor_new_gel`). ![feats](assets/samples_40_feats.png) *FEATS samples after empty-frame removal — every frame now shows a real contact.* **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. The entire conversion script lives in this repo at [`scripts/make_parquet_v2.py`](scripts/make_parquet_v2.py). It implements the per-source decoders, the validity filter, the perceptual-hash dedupe, the 200K-per-source budget, and the parquet sharding. The CLI: ```bash python make_parquet_v2.py probe # measure dynamism + empty fraction python make_parquet_v2.py process # full pipeline + write parquet shards python make_parquet_v2.py stats # row counts across all subsets ``` `` is one of `gelslam`, `tactile_tracking`, `real_tactile_mnist`, `feelanyforce`. ## Statistics at a glance Composition across the 8 subsets, coloured by gel variant: ![composition](assets/composition.png) Image resolution — the dataset retains both GelSight Mini native modes. Filter by `height`/`width` if you need a uniform-resolution pool: ![resolution](assets/resolution_distribution.png) FEATS normal-force distribution and indenter-shape mix (the only subset with force labels): ![force](assets/force_distribution.png) py3DCal probe-position coverage — confirms the dense calibration grid: ![threedcal coverage](assets/threedcal_coverage.png) Real Tactile MNIST · digit-class balance (used as a sanity check that the upstream touch sampling was uniform across digits 0–9): ![rtm digits](assets/rtm_digit_distribution.png) Per-channel pixel-value distribution comparing **kept** vs **rejected** frames for the three subsets that were filtered most recently (real_tactile_mnist with I_min=12; sim_tactile_mnist and sim_starstruck with I_min=10). The red/blue separation per channel confirms the filter discriminates contact-bearing frames from at-rest frames cleanly: ![pixel value distribution](assets/pixel_value_distribution.png) ## Balance metrics We report two complementary scores along four bucket axes — **domain** (real/sim), **sensor_id** (13 distinct physical sensor configurations), **object_id** (every unique object instance), and **gel_variant** (markered/markerless): - **Normalized Shannon entropy** `H̃ = H/log(B) ∈ [0,1]`. Higher = more uniform across buckets. - **Effective Sample Size** `ESS = (Σn)²/Σn²`. Effective number of equally-weighted buckets — `100%` of B means perfectly uniform. | Axis | B (buckets) | H̃ | ESS | ESS / B | |---|---:|---:|---:|---:| | domain | 2 | **0.95** | 2 | 94% | | sensor | 13 | 0.69 | 5 | 35% | | gel variant | 2 | 0.38 | 1 | 58% | | object | **8,459** | **0.78** | 239 | 2.8% | | 4-tuple bucket | 8,478 | 0.79 | 271 | 3.2% | The dataset has **~63/37 real/sim** balance (H̃ = 0.95 along the domain axis) and covers **8,459 unique object instances** across 13 physical sensor configurations. The gel-variant axis is skewed toward markerless (92.5% vs 7.5% markered) because most upstream sources only release markerless captures. See `assets/balance_report.json` for the full bucket histograms and per-axis distributions. ## Composition | Subset | Source dataset | Frames | Gel | Has labels | |-------------------|------------------------------------------|----------:|------------|------------------------------------------| | `fota_labeled` | FoTA — *panda_warped* still captures | **29,494** (66% markerless, 34% markered) | mixed¹ | end-effector x,y,z + quaternion | | `fota_unlabeled` | FoTA — same captures, video frames | **266,761** train-only (re-sampled with looser dedupe; see ³) | 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 | **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 | **30,956** (26K train + 5K test; re-extracted from **video upstream** with peak-contact-per-touch picker, I_min=12) | markerless | digit class (0–9) + print id | | `feelanyforce` | FeelAnyForce force-controlled indentations | **50,997** | markerless² | object name | | `sim_tactile_mnist` | **SIM** · Taxim-rendered Mini imagery of digit touches | **150,601** (102K train + 49K test; raw upstream + stride=2 + I_min=10) | markerless | digit class + episode | | `sim_starstruck` | **SIM** · Taxim-rendered Mini imagery of star objects | **166,104** (150K train + 16K test; raw upstream + stride=3 + I_min=10) | markerless | episode | | `unit` | UniT continuous 3D-pose tracking | **11,340** | markerless | 3D-pose target (x,y,z,yaw) | | `tacquad` | TacQuad quad-sensor benchmark (Mini stream) | **12,195** (5K indoor + 4K outdoor + 3K fine, 181 objects) | markerless | object name + environment | | `tvl` | TVL Touch-Vision-Language paired tactile+RGB+caption | **209,795** (HCT + SSVTP subsets; multi-resolution: 640×480 + 320×240) | markerless | session id + paired RGB + GPT-4V caption (RGB+text not stored) | 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. ³ `fota_unlabeled` was **uniformly stride-subsampled** from 516,523 → 66,761 frames (~12.9 % retention) and consolidated as **train-only** on 2026-05-18. Reason: only 11 objects across 60 captures means within-capture frames are near-duplicate gripper-trajectory snapshots, and the raw subset was ~70 % of the aggregated pretraining pool. Because this subset has only `obj_name` as a label (no pose / force / ground-truth contact) there is nothing to benchmark, so the prior train/val partition served no purpose and has been dropped. Every (object, init_pose, side) combination is still represented; the subset's share of the aggregated pool drops to ~7 %. ```python ds = load_dataset("yxma/gelsight-mini-pretrain", "fota_labeled", split="train") markerless = ds.filter(lambda r: not r["markered"]) markered = ds.filter(lambda r: r["markered"]) ``` ## Sample images ### `fota_labeled`  ·  29,494 frames  ·  mixed gel  ·  +6DoF pose One labeled still captured at every recorded end-effector pose along a Franka Panda trajectory pressing one of 13 household objects into the gel. The arc-shaped imprints are tactile signatures of objects (here, plier handles, clamps, knives, etc.). FoTA used **both markered and markerless gels** — use the `markered` column to filter. Random sample (mixed): ![fota_labeled mixed](assets/samples_40_fota_labeled.png) Per gel variant: | markerless (66% of the data) | markered (34% of the data) | |:---:|:---:| | ![fota markerless](assets/samples_40_fota_labeled_markerless.png) | ![fota markered](assets/samples_40_fota_labeled_markered.png) | ### `threedcal`  ·  36,270 frames  ·  markerless  ·  +xyz pose A motorised sphere indenter is pressed into the gel at **1,209 different (x, y) positions** at a fixed 3 mm depth. The bright spot moves as the probe walks across the sensor surface — useful for learning a calibrated position→appearance mapping. ![threedcal](assets/samples_40_threedcal.png) ### `feats`  ·  16,711 frames  ·  **markered**  ·  +force Six indenter shapes (sphere, cuboid, cylinder, pyramid, cross, plus one "unknown" set of held-out probes), each pressed into a markered (dotted) GelSight Mini gel. Provides f_x, f_y, f_z forces and a 32×24 depth grid per image. Forces span from ~0 N (light touch) to **−73 N** (heavy normal compression). ![feats](assets/samples_40_feats.png) > **⚠️ FEATS has two physical gel variants.** A new column `gel_variant` distinguishes them: > - `"black_dot"` — the standard dotted Mini gel used for `train`, `val`, `test`, `test_unknown_indenters`, `test_diff_sensor_old_gel`. > - `"different"` — a **second Mini sensor with a different gel** (smaller / dimmer / differently-coloured markers) used only in the `test_diff_sensor_new_gel` split. This split was designed by the FEATS authors to test cross-gel generalisation. Visually, marker dots are less prominent here. > > **No-contact frames removed.** The upstream FEATS dataset included ~5,300 frames where the indenter was hovering off the gel (|f_z| < 0.5 N). These have been filtered out here to give a cleaner pretraining set. Original counts (with the empty frames) were 22,013; current counts are 16,711. > > ![feats_diff_gel](assets/samples_feats_diff_gel.png) > > See `assets/samples_feats_by_split.png` for one example per (split, indenter) pair. ### `fota_unlabeled`  ·  66,761 frames  ·  **train-only** 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`  ·  60,982 frames  ·  markerless  ·  +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_40_gelslam.png) ### `tactile_tracking`  ·  1,143 frames  ·  markerless  ·  +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_40_tactile_tracking.png) ### `real_tactile_mnist`  ·  56,723 frames  ·  markerless  ·  +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. The upstream release ships each touch as a short video clip; here we keep **one middle-frame per touch video** (near peak contact). After running the unified area+intensity contact filter with **I_min = 12** (calibrated for real-image noise floor; see `assets/pixel_value_distribution.png`), we retain 56,723 frames (28.4 % of the 200K raw frames; ~1.5 % background-diversity keep rate). 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_40_real_tactile_mnist.png) ### `feelanyforce`  ·  50,997 frames  ·  markerless  ·  +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_40_feelanyforce.png) ## Useful statistics ### `fota_labeled` — pose coverage - **13** distinct contact objects · **5** initial poses · **15,148 unique (object, pose, side) captures** - Frames split equally between left / right gripper finger: **14,747 / 14,747** - End-effector range: `x ∈ [−25.7, 129.1] mm` · `y ∈ [−137.3, 137.3] mm` · `z ∈ [−38.6, 38.6] mm` - Top objects by frame count: |object|frames| |---|---:| |tapemeasure | 4,800 | |whiteclamped| 3,600 | |blackclamp | 3,016 | |blackclampclosed | 2,772 | |key1 | 2,400 | |plierhandle | 2,400 | |foldingknife | 2,156 | |wrench | 2,134 | |blackclampedclosed | 1,922 | |printedstrawberry | 1,800 | ### `threedcal` — calibration grid - All 36,270 frames are sphere indentations at **z = 3 mm penetration** - `x ∈ [0, 19] mm` · `y ∈ [0, 15] mm` · **1,209 unique (x,y) grid positions** - ~30 repeated frames per position (lighting noise / repeat trials), useful for variance estimation ### `feats` — indenter & force coverage | Shape | Sizes (mm) | Total frames | |---|---|---:| | cuboid | 2, 7, 10, 12, 15, 20 | 4,683 | | sphere | 10, 15, 20 | 2,883 | | cylinder | 8, 10 | 1,291 | | cross | 15 | 894 | | pyramid | 10 | 854 | | *(unannotated)* | — | 11,408 | - Normal force range: `f_z ∈ [−73.3, 0.0] N` (mean **−9.30 N**, std **10.66 N**) - Shear force range: `f_x ∈ [−4.86, 4.86] N`, `f_y ∈ [−5.89, 5.87] N` - Six labeled **splits** including out-of-distribution tests: - `train` (15,670), `val` (921), `test` (1,845) — in-distribution - `test_diff_sensor_new_gel` (395) — new physical gel - `test_diff_sensor_old_gel` (393) — different sensor unit - `test_unknown_indenters` (2,789) — held-out probe shapes ### `fota_unlabeled` — characterisation - **11 distinct objects** (subset of `fota_labeled`'s 13) - **60 captures** (11 objects × ~5 init-poses × 2 gripper sides) - Roughly balanced left / right (~50/50) - ~1000 frames per capture after stride-subsampling³ ## Unified schema Every row, regardless of source, has the same columns. Optional fields are `null` when not applicable. | Column | Type | Description | |---|---|---| | `image` | image (binary) | tactile RGB frame, JPEG bytes; auto-decoded by `datasets` to `PIL.Image` | | `image_format` | string | always `"jpeg"` | | `source` | string | one of `"fota_labeled"` / `"fota_unlabeled"` / `"3dcal"` / `"feats"` | | `markered` | bool | does the gel have tracking dots? | | `capture` | string | per-source capture / scene / probe identifier | | `split` | string | dataset split (e.g. `train`, `val`, `test_unknown_indenters`, …) | | `height`, `width`| int32 | image dimensions in pixels | | `obj_name` | string | (FoTA) which object was pressed | | `init_pose` | int32 | (FoTA) initial-pose index for this capture | | `side` | string | (FoTA) `"left"` or `"right"` gripper finger | | `x_mm`, `y_mm`, `z_mm` | float32 | probe / end-effector position | | `quat_x..w` | float32 | (FoTA labeled) end-effector orientation | | `indenter` | string | (FEATS) probe shape, e.g. `"sphere"` | | `indenter_param` | string | (FEATS) probe size in mm | | `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. Sim frames are noise-free, so the contact filter is applied with a lower threshold **I_min = 10** (vs I_min = 12 / 15 for real subsets), yielding ~93 % retention for `sim_tactile_mnist` and ~97 % for `sim_starstruck`. See `assets/pixel_value_distribution.png` for the per-channel pixel-value histograms showing how the filter discriminates contact vs at-rest frames in each subset. ## Recommended uses - **Self-supervised pretraining** (VAE / MAE / SimCLR / DINO): use `fota_unlabeled + fota_labeled + threedcal` (markerless, **~133K frames**). Hold `feats` out since its dotted-gel appearance will dominate the reconstruction objective if mixed in. - **Force / shear regression**: fine-tune on `feats` with its `f_{x,y,z}` labels. - **Pose estimation**: fine-tune on `fota_labeled` (xyz + quat) or `threedcal` (xy + depth). - **Marker invariance studies**: train markerless ↔ test on `feats`, or use a marker-mask augmentation. ## Provenance and citation This dataset is a re-packaging of existing public datasets — please cite the upstream sources if you use the data: - **FoTA** — *Foundation Tactile*, Zhao et al., 2024. [HF dataset](https://huggingface.co/datasets/alanz-mit/FoundationTactile), [GitHub](https://github.com/alanzjl/t3), [arXiv:2406.13640](https://arxiv.org/abs/2406.13640) · *MIT License*. - **3D Cal (py3DCal)** — Kota, Shah, Colgate, Reardon (2025). [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*. - **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 `threedcal` are decoded losslessly and re-encoded; FEATS `.npy` dicts have their `gs_img` array extracted and saved as JPEG; FoTA WebDataset shards are unpacked and re-encoded. Metadata is preserved verbatim. - *FeelAnyForce* (Sharei et al., 2024) ~200K markerless frames may be added in a future revision pending license verification with the upstream authors. ## License This aggregated release is released under **CC-BY-4.0** (the most restrictive license among the included sources). Cite the component datasets above. ## Acknowledgments Thanks to the FoTA, py3DCal, and FEATS authors for releasing their data publicly.