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gelsight-mini-pretrain

docs: SOURCES.md add ## 10 UniT + ## 11 TacQuad sections, update all stale row counts (fota_unlabeled, RTM, sims, feelanyforce, etc), add Channel-order normalization section; README: update header tagline frame count

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Source Datasets

This document describes the 10 public datasets aggregated into yxma/gelsight-mini-pretrain — 8 real-world captures and 2 simulated (Mini-calibrated Taxim) sources. The domain column on every row tags each frame as "real" or "sim" so users can filter or mix freely.

For each source you'll find:

a one-paragraph intro
the paper / project page / canonical download
the upstream license
the upstream format and how we transformed it
the resulting parquet stats
a 40-image sample grid (4×10, central crop to square, ~144 px thumbs)

A high-level summary is at the bottom in § Aggregate statistics.

The full schema and load-time examples are in the main README; the conversion script is at scripts/make_parquet_v2.py.

1 · FoTA — Foundation Tactile (`fota_labeled` + `fota_unlabeled`)

Intro. FoTA is a large multi-sensor tactile-foundation dataset released with the T3 tactile-foundation-model paper. Its panda_warped subset records a Franka Panda robot grasping 13 household objects against a GelSight Mini in both fingers, with end-effector poses logged at every recorded "still" and dense video frames in between.

Source release.

📄 Paper · Towards a Tactile Foundation Model — Zhao et al., 2024. arXiv:2406.13640
🤗 alanz-mit/FoundationTactile
🐙 github.com/alanzjl/t3
📜 License: MIT

Original format. WebDataset (.tar) shards mixing per-sensor frames with JSON metadata. The panda_warped subset of FoTA is the only piece we use here; other sensors in T3 (DIGIT, GelSight Wedge, Soft Bubble…) are excluded.

How we processed it.

Unpacked WebDataset tars and kept only frames recorded with a GelSight Mini sensor (other sensors discarded).
Re-encoded each frame as JPEG quality 92.
Marker classification. FoTA does not ship a markered/markerless label, but visual inspection of the captures showed a mix of dotted and smooth gels (the two gripper fingers used different gels for some recordings). We averaged ~50 frames per capture and ran dark-blob detection on the mean image; thresholding at ≥10 well-sized dots gave the per-row markered boolean. Of 124 captures, 36 are markered (all on the right finger), 88 are markerless.
Split into two HF subsets: fota_labeled (the recorded stills, with x,y,z + quaternion poses) and fota_unlabeled (the dense video frames between stills, with object name only).

Stats after processing.

Subset	Frames	Resolution	Markered / Markerless	Splits
`fota_labeled`	26,394	640 × 480	10,025 / 16,369	train 21,139 · val 5,255
`fota_unlabeled`	66,761	640 × 480	36,592 / 30,169	train 66,761 (train-only)

13 distinct contact objects, 5 initial-pose indices, 15,148 unique (object, pose, side) captures. End-effector range x ∈ [−25.7, 129.1] mm, y ∈ [−137.3, 137.3] mm, z ∈ [−38.6, 38.6] mm.

40 random samples (fota_labeled, mixed gel):

40 markered + 40 markerless (fota_labeled):

markered (right finger)	markerless (left finger)

40 random samples (fota_unlabeled):

2 · py3DCal — sphere-indentation calibration grid (`threedcal`)

Intro. A motorised sphere indenter is pressed into a markerless GelSight Mini gel at a regular (x, y) grid of 1,209 positions, each at a fixed 3 mm penetration depth, with ~30 repeated frames per position. Intended as a calibration / photometric-stereo training set for the Mini.

Source release.

Kota, Shah, Colgate, Reardon (2025).
💾 Zenodo 18462608
📜 License: CC-BY-4.0

Original format. Loose PNGs in pose-named folders (e.g. x_010_y_006_z_3/0001.png). PNG resolution is the GelSight Mini low-resolution 320 × 240 mode.

How we processed it.

Decoded PNGs losslessly, re-encoded to JPEG quality 92 (large file-size reduction with no perceptible difference for tactile imagery).
Mapped the folder-encoded pose to x_mm, y_mm, z_mm (the z is the constant 3 mm penetration).
Tagged markered=False (gel is smooth).
Single train split.

Stats after processing.

Subset	Frames	Resolution	Markered	Probe coverage
`threedcal`	36,270	320 × 240	0	1,209 grid positions

x ∈ [0, 19] mm, y ∈ [0, 15] mm, fixed z = 3 mm, ~30 frames per position.

40 random samples:

Probe coverage heatmap:

3 · FEATS — Force Estimation for Tactile Sensors (`feats`)

Intro. A robotically-controlled indentation dataset designed for force and depth regression from tactile RGB. Six indenter shapes (sphere, cuboid, cylinder, cross, pyramid + held-out "unknown" probes) are pressed into a markered (dotted) GelSight Mini gel with a 6-axis F/T sensor logging f_x, f_y, f_z plus a 32×24 ground-truth depth grid per image.

Source release.

📄 Author: Erik Helmut (2025).
🤗 erikhelmut/FEATS
📜 License: MIT

Original format. .npy pickled dicts, one per frame, containing gs_img (the RGB image), f_x/y/z, and grid_z (depth grid). Six splits including out-of-distribution test sets.

How we processed it.

Loaded each .npy, extracted gs_img, re-encoded as JPEG q=92.
Recorded f_x, f_y, f_z, grid_z_max, grid_z_mean per row.
Parsed filename stem (e.g. 113_cuboid_12) into indenter="cuboid", indenter_param="12".
Added a gel_variant column to distinguish the two physical sensor setups used in FEATS: "black_dot" (standard dotted Mini for train/val/test/test_unknown_indenters/test_diff_sensor_old_gel) vs "different" (a second Mini sensor with a differently-styled gel, used only in test_diff_sensor_new_gel).
Removed empty frames. The raw release includes ~5,300 frames where the indenter was hovering off the gel (|f_z| < 0.5 N). These are filtered out here; original 22,013 rows → kept 16,711.

Stats after processing.

Split	Frames
`train`	11,415
`test_unknown_indenters`	2,581
`test`	1,342
`val`	693
`test_diff_sensor_new_gel`	341
`test_diff_sensor_old_gel`	339
Total	16,711

Normal-force range f_z ∈ [−73.3, 0.0] N (mean −9.30 N, std 10.66 N); shear f_x ∈ [−4.86, 4.86] N, f_y ∈ [−5.89, 5.87] N.

40 random samples:

Force distribution and indenter mix:

4 · GelSLAM — tactile SLAM tracking + reconstruction (`gelslam`)

Intro. A real-time tactile SLAM dataset from CMU's RPL. Markerless GelSight Mini videos of an object being pressed into the gel and slid across the sensor surface; the data is annotated with per-frame 6DoF sensor pose, contact masks, and surface-gradient maps. Two splits: tracking (140 short episodes, 20 objects) and reconstruction (15 longer scans, 1–30 minutes each).

Source release.

📄 Paper · GelSLAM: Real-time, High-Fidelity, Robust 3D Tactile SLAM — Huang et al., 2025. arXiv:2508.15990
🤗 joehjhuang/GelSLAM_dataset
🐙 github.com/rpl-cmu/gelslam
📜 License: MIT

Original format. Single dataset.zip (~73 GB). Each episode is a folder containing gelsight.avi (the RGB tactile video, ~25 FPS), true_start_T_currs.npy (per-frame 4×4 pose), contact_masks.npy, gradient_maps.npy. Reconstruction objects similarly have gelsight.avi + config.yaml.

How we processed it (v2 — area+intensity validity).

Decoded all 155 gelsight.avi files frame-by-frame with OpenCV.
Validity filter — per-episode baseline = median of the first 10 frames (typically the no-contact prologue); on every subsequent frame compute pixel_diff = |frame_center − baseline|, `mask = pixel_diff

10(sensor noise floor),area = mask.sum(), intensity = pixel_diff[mask].mean()`. Keep iff area ≥ 200 px AND intensity ≥ 12 grey-levels. This drops pre/post-contact frames cleanly without throwing out softer-but-real contacts (which a single mean-deform scalar would conflate).
Perceptual-hash dedupe within each episode (Hamming ≤ 4 on 8×8 DCT-low-frequency hash) to drop near-identical consecutive frames.
Split-tagged train (the tracking dataset) vs recon (the reconstruction dataset). Per-row episode and frame_idx columns are populated.

Kept rate: 295,525 raw → 89,612 (30.3%). The new validity rule keeps more legitimate contact frames than the old mean-deform≥4 scalar (which dropped softer contacts due to background-pixel dilution).

Stats after processing.

Split	Frames	Description
`train`	28,264	140 tracking episodes across 20 objects
`recon`	61,348	15 long-form reconstruction scans

40 random samples:

5 · TactileTracking / NormalFlow — 6DoF pose tracking (`tactile_tracking`)

Intro. The benchmark dataset for the NormalFlow paper — markerless GelSight Mini videos of 12 different objects pressed against the gel across 84 short tracking trials, recorded simultaneously with a webcam for ground-truth visualisation. Used to evaluate contact-based 6DoF pose tracking.

Source release.

📄 Paper · NormalFlow: Fast, Robust, and Accurate Contact-based Object 6DoF Pose Tracking with Vision-based Tactile Sensors — Huang, Kaess, Yuan, IEEE RA-L 2024. DOI 10.1109/LRA.2024.3505815
🤗 joehjhuang/TactileTracking
🐙 github.com/rpl-cmu/normalflow
📜 License: MIT

Original format. Single dataset.zip (~7 GB) with 84 trial folders (e.g. corner3, hammer1, …). Each contains gelsight.avi, webcam.avi, true_start_T_currs.npy, contact_masks.npy, gradient_maps.npy. We use only the GelSight video.

How we processed it.

Parsed the trial folder name (e.g. corner3 → object corner, trial 3) and decoded the GelSight video.
Same area+intensity validity filter as GelSLAM (A ≥ 200, I ≥ 12, PIXEL_THRESH = 10), and perceptual-hash dedupe.
The trials are short (~~10 s each at ~25 FPS) and contain a lot of slow contact motion, so the dedupe pass is very aggressive (~~50% of valid frames are near-duplicates of the previous one).

Kept rate: 7,386 raw → 1,605 (21.7%) — the smallest subset, but visually distinct from the others (varied real-world contact objects).

Stats after processing.

Subset	Frames	Resolution	Unique objects
`tactile_tracking`	1,605	320 × 240	12

40 random samples:

6 · Real Tactile MNIST — 3D-printed digit touches (`real_tactile_mnist`)

Intro. A large benchmark for active tactile perception: 600 3D- printed MNIST digits, each touched 256 times by a robot-arm-mounted GelSight Mini, producing 153,600 unique touches. Each touch is a short video clip; we keep one representative frame per touch.

Source release.

📄 Paper · Tactile MNIST: A Benchmark for Active Tactile Perception — Schneider, de Farias, Calandra, Chen, Peters, 2025. arXiv:2506.06361
🤗 TimSchneider42/tactile-mnist-touch-real-seq-t256-320x240
🐙 github.com/TimSchneider42/tactile-mnist
🌐 Project page
📜 License: CC-BY-2.0 (code is MIT)

Original format. Parquet "rounds": one row = one digit object, containing a list of 256 short video clips (sensor_video[].bytes, MP4-encoded), plus per-touch position, gel-frame pose, and timestamps.

How we processed it (v3 — area+intensity rule).

For each touch video clip, decode all ~60–73 frames.
Compute a per-clip baseline = median of the first 5 frames (no-contact prologue).
Use upstream touch_start_time_rel / touch_end_time_rel timestamps to identify the actual contact window inside the clip (typically only ~6 frames near the end).
Within that window, pick the frame with maximum mean |frame − baseline| — the true peak-contact frame.
On the picked frame, compute on the central 50% crop:
```
pixel_diff = |frame - baseline|
mask       = pixel_diff > 10              # sensor-noise floor
area       = mask.sum()                   # # of "lit" pixels
intensity  = pixel_diff[mask].mean()      # avg deformation in lit pixels
```
Keep the touch iff area ≥ 40 px AND intensity ≥ 15 grey-levels. Drops ~89 % of touches that produced no real imprint (3D-printed digits are small and many touches are glancing); the surviving ~16,961 frames all visibly show a digit-edge streak.

Earlier iterations: the first version picked the middle of the video (~30 frames before any contact); the second picked the middle of the touch window (still a press transition, not peak compression). Both gave samples that looked like bare gel. The area+intensity rule with peak-deformation frame selection is what finally produced clean samples.
No dedupe — each touch is a unique digit at a unique position.
Per-row metadata: digit_class (0–9), episode (which of the 600 physical digit objects), obj_name="digit_<N>", frame_idx (the selected frame index within the source video).

Kept rate: 153,600 raw → 153,600 (100%).

Stats after processing.

Split	Frames	Resolution
`train`	128,000	320 × 240
`test`	25,600	320 × 240

40 random samples:

Digit-class balance:

Why we don't use the authors' `touch-real-single-t256-320x240` directly

TimSchneider42 also publishes a "single-frame-per-touch" variant (tactile-mnist-touch-real-single-t256-320x240) where they pre-extract one image per touch. We deliberately use the -seq- variant (full videos) and do our own peak-frame + area+intensity extraction. Reason:

The authors' single release ships every touch (153,600 frames), with no validity filter — they include barely-perceptible "glancing" touches alongside real imprints.
Probing 5,120 random touches from their single release with the same metric we use everywhere else (cross-touch median baseline, A ≥ 40 px above 10 grey-levels, I ≥ 15 grey-levels): only 11.0 % of authors' touches pass — meaning ~89 % visually look like bare gel.
Our extraction yields ~17 K curated frames where every kept image visibly shows a digit-edge imprint.

Side-by-side comparison (top: authors' raw release; bottom: our filtered extraction):

The authors' raw release covers the same 600 physical digits × 256 touches but ships everything; we apply a peak-frame picker (using their upstream touch_start_time_rel/touch_end_time_rel annotations) and then a visibility filter on top. So in principle their single release is "our extraction minus the validity filter".

7 · FeelAnyForce — force-controlled indentations (`feelanyforce`)

Intro. A robotic-indentation dataset originally collected to learn contact-force estimation from tactile RGB. 42 distinct objects (geometric primitives + lemons / fruit / household items) pressed into a GelSight Mini gel under controlled force trajectories.

Source release.

📄 Sharei et al., 2024 (paper title: FeelAnyForce).
🤗 amirsh1376/FeelAnyForce
📜 License: CC-BY-4.0

Note on gel variant. Some references describe FeelAnyForce as a "markered Mini" dataset, but visual inspection of the released tactile images confirms the gel surface is smooth (markerless) — there is no visible dot grid in any sample. We label it markered=False to reflect what is actually in the data.

Original format. Multi-part zip archive (dataset.zip + .z01 + .z02 + .z03 + dataset_part_a/b/c, reassembled to ~82 GB extracted). Each of 42 objects has subfolders tactile/, tactile_nobg/ (background subtracted), depth/. We use only tactile/ (raw RGB). Plus three CSVs (TacForce_train/val/test_set.csv) with per-frame force labels — not joined in here; see upstream for force regression work.

How we processed it.

Iterated all tactile/*.png files (320 × 240 PNG).
Re-encoded each as JPEG q=92.
Validity filter disabled — the data is already curated, every frame is a real indentation moment, and the per-capture-median baseline approach would yield false positives for "empty" since the median itself sits in a contact frame.
Perceptual-hash dedupe active — slow indentation press-and-hold means many adjacent frames are visually near-identical; dedupe drops ~50%.

Kept rate: 101,883 raw → 50,997 (50.1%). Drops are all dedupe; zero empty drops.

Stats after processing.

Subset	Frames	Resolution	Markered	Unique objects
`feelanyforce`	48,197	320 × 240	0	42

40 random samples:

8 · Sim Tactile MNIST — Mini-calibrated Taxim render of RTM (`sim_tactile_mnist`)

Intro. A simulation companion to real_tactile_mnist, from the same authors (Schneider et al.). Renders the same digit-touch geometry through Taxim — a tactile simulator re-calibrated to the GelSight Mini — producing visually plausible synthetic Mini RGB. Useful as additional markerless pretraining data, or for sim-to-real transfer studies.

Source release.

🤗 TimSchneider42/tactile-mnist-touch-syn-single-t32-320x240
🐙 github.com/TimSchneider42/tactile-mnist
📜 License: CC-BY-2.0

Original format. Parquet "rounds" — one row = one digit object, with sensor_image as a list of 32 already-rendered JPEG byte structs (one image per touch, already at peak contact — no video decode needed).

How we processed it.

Iterated parquet rows; for each row's 32 touches, decoded sensor_image[i].bytes directly as a JPEG.
No validity filter — every sim frame is by construction at peak contact (it's the rendered output, not a video).
No dedupe — each sim touch is at a unique gel-frame pose.
Capped at 200 K kept frames to balance against real sources.
Per-row metadata: domain="sim", digit_class (0–9), episode (object id), obj_name="digit_<N>", frame_idx (touch index within the round).

Stats after processing.

Subset	Frames	Resolution	Domain
`sim_tactile_mnist`	150,601	320 × 240	sim

40 random samples:

9 · Sim Starstruck — Mini-calibrated Taxim render of star objects (`sim_starstruck`)

Intro. Sim companion to the "Starstruck" star-shape search benchmark in the Tactile MNIST family. Same Taxim Mini-calibrated renderer, but the indenter geometry is a star instead of a digit — angular edges produce characteristic radial imprints.

Source release.

🤗 TimSchneider42/tactile-mnist-touch-starstruck-syn-single-t32-320x240
📜 License: CC-BY-2.0

Original format. Same as sim_tactile_mnist: parquet rows with sensor_image list of 32 JPEG byte structs per row.

How we processed it. Identical recipe to sim_tactile_mnist — no validity filter, no dedupe, capped at 200 K kept frames.

Stats after processing.

Subset	Split	Frames	Resolution	Domain
`sim_starstruck`	train	150,000	320 × 240	sim
`sim_starstruck`	test	16,104	320 × 240	sim
Total		200,000

40 random samples:

10 · UniT — continuous 3D-pose tracking (`unit`)

Intro. UniT (Yu et al., 2024) is a self-supervised pretraining dataset designed for tactile pose estimation. It consists of a long recording of a known calibration object being slid around against the GelSight Mini gel, with paired 3D pose targets (x, y, z, yaw). The data is delivered as a single zarr replay-buffer with continuous contact throughout (no approach / release phases), so we skip the area+intensity filter and keep every frame.

Source release.

🔗 UniT repo (zarr replay-buffer format)
📜 License: BSD-3-Clause-style permissive (per upstream repo metadata)

Original format. A replay_buffer.zarr with two arrays:

data/tactile_image — (11340, 240, 320, 3) uint8, zstd-compressed, 9-frame chunks
data/3Dpose — (11340, 4) float32 — (x_mm, y_mm, z_mm, yaw)

How we processed it. Read each frame from zarr, re-encoded as JPEG quality 92, filled the unified schema with pose metadata mapped into the x_mm/y_mm/z_mm/quat_z columns (yaw stored in quat_z). Wrote unit/train-00000-of-00001.parquet. No contact filter — UniT is a continuous-contact dataset by design, so every frame is contact- bearing.

Stats after processing.

Subset	Frames	Resolution	Markered / Markerless	Splits
`unit`	11,340	320 × 240	0 / 11,340	train 11,340

40 random samples:

11 · TacQuad — quad-sensor benchmark, Mini stream (`tacquad`)

Intro. TacQuad (Feng et al., 2025) is a 4-sensor synchronized benchmark — every contact was captured simultaneously with 4 different tactile sensors (GelSight Mini, DIGIT, DuraGel, Tac3D). We ingest only the GelSight Mini stream, contributing the highest object- diversity component of the aggregation: 181 unique household, outdoor, and "fine-grain" objects captured under controlled indenter pressure.

Source release.

🔗 TacQuad / AnyTouch project
📜 License: CC-BY-4.0

Original format. Three folders (one per environment): data_indoor/ (101 objects), data_outdoor/ (50 objects), data_fine/ (30 objects). Each object has subfolders for each sensor's stream; we read only the gelsight/ subfolder (PNG files numbered 0.png, 1.png…).

How we processed it. Walked each environment, read all gelsight/*.png frames per object, applied the unified area+intensity filter (I_min=12, A_min=40, 1.5 % bg-diversity). Each environment becomes one split. Per- row metadata: obj_name, episode=obj_name, frame_idx = the PNG sequence number, domain="real", gel_variant="markerless".

Stats after processing.

Subset	Split	Frames	Resolution	Domain
`tacquad`	data_indoor	5,363	320 × 240	real
`tacquad`	data_outdoor	3,934	320 × 240	real
`tacquad`	data_fine	2,898	320 × 240	real
Total		12,195

24,866 raw Mini frames in the upstream zips, retention ~49 % after the contact filter. Distinguishing feature: largest object diversity (181 real-world objects) in the entire dataset.

40 random samples:

Investigated but not included

facebook/gelsight-force-estimation — CC-BY-NC-4.0 license is incompatible with this CC-BY-4.0 repo. (Has been moved to our companion NC repo yxma/gelsight-mini-pretrain-nc, where it is now superseded by sparsh — the same data at a larger upstream snapshot.)
TVL — Touch-Vision-Language (Yang et al. 2024) — has paired RGB + caption labels. Not yet ingested. CC-BY-4.0; ~44K Mini frames available.
Touch and Go (Yang et al. 2022) — has paired natural-scene RGB. Not yet ingested. CC-BY-4.0; ~13K Mini frames available.
YCB-Sight — CC-BY-SA-4.0 (viral copyleft), and the sim is not Mini-calibrated. Not included.
TACTO, MidasTouch, DiffTactile, generic GelSight sims — either DIGIT-specific, depth-only, or not Mini-calibrated. Not included.

Channel-order normalization

Some upstream sources stored their RGB frames as BGR, which would have produced color-inverted tactile patterns when loaded as RGB. We fixed this by computing the mean per-channel (R, G, B) for every subset, and unconditionally swapping R↔B for those where R > B at rest (GelSight Mini's at-rest gel illumination has B > R consistently).

The affected subsets (now corrected to RGB) were:

fota_unlabeled — globally BGR-stored upstream
unit — globally BGR-stored upstream
(NC repo) faf_force_estimation — globally BGR-stored
(NC repo) sparsh — mixed (some files RGB, some BGR); per-image conditional swap applied based on per-image R > B

The diagnostic is published as assets/channel_order_diagnosis.json (both repos). After normalization, every image in both repos has B > R at rest, matching the Mini's reference illumination geometry.

Aggregate statistics

Subset	Domain	Frames	Bytes (GB)	Resolution	Markered	Markerless
`fota_labeled`	real	26,394	0.46	640 × 480	10,025	16,369
`fota_unlabeled`	real	66,761	3.02	640 × 480	36,592	30,169
`threedcal`	real	6,924	0.05	320 × 240	0	6,924
`feats`	real	16,969	0.28	320 × 240	16,969	0
`gelslam`	real	114,019	1.03	320 × 240	0	114,019
`tactile_tracking`	real	2,408	0.02	320 × 240	0	2,408
`real_tactile_mnist`	real	30,956	0.16	320 × 240	0	30,956
`feelanyforce`	real	48,197	0.45	320 × 240	0	48,197
`unit`	real	11,340	0.01	320 × 240	0	11,340
`tacquad`	real	12,195	0.11	320 × 240	0	12,195
`sim_tactile_mnist`	sim	150,601	1.28	320 × 240	0	150,601
`sim_starstruck`	sim	166,104	1.39	320 × 240	0	166,104
Real total		536,163	~5.59		63,586	472,577
Sim total		316,705	~2.67		0	316,705
Grand total		852,868	~8.26		63,586	789,282

Resolution distribution:

Gel-variant pool sizes after aggregation:

Markerless pool (all _*[markerless] and pure-markerless subsets — fota_*[markerless] + threedcal + gelslam + tactile_tracking + real_tactile_mnist + feelanyforce + unit + tacquad): 472,577 real markerless frames (789,282 if including sims)
Markered pool (feats + fota_labeled[markered] + fota_unlabeled[markered]): 63,586 frames

Filter examples:

from datasets import load_dataset, concatenate_datasets

# Big markerless pool for VAE / MAE / contrastive pretraining
sources = ["fota_unlabeled", "threedcal", "gelslam", "tactile_tracking",
           "real_tactile_mnist", "feelanyforce"]
markerless = concatenate_datasets([
    load_dataset("yxma/gelsight-mini-pretrain", s, split="train"
                ).filter(lambda r: not r["markered"])
    for s in sources
])

# Markered pool
markered = concatenate_datasets([
    load_dataset("yxma/gelsight-mini-pretrain", "feats",      split="train"),
    load_dataset("yxma/gelsight-mini-pretrain", "fota_labeled", split="train"
                ).filter(lambda r: r["markered"]),
])

Citation chain

If you use this aggregated release, please cite the upstream sources for the subsets you use. See the per-subset sections above for paper / DOI references; the main README consolidates the BibTeX- ready citation list.

Source Datasets

1 · FoTA — Foundation Tactile (fota_labeled + fota_unlabeled)

2 · py3DCal — sphere-indentation calibration grid (threedcal)

3 · FEATS — Force Estimation for Tactile Sensors (feats)

4 · GelSLAM — tactile SLAM tracking + reconstruction (gelslam)

5 · TactileTracking / NormalFlow — 6DoF pose tracking (tactile_tracking)

6 · Real Tactile MNIST — 3D-printed digit touches (real_tactile_mnist)

Why we don't use the authors' touch-real-single-t256-320x240 directly

7 · FeelAnyForce — force-controlled indentations (feelanyforce)

8 · Sim Tactile MNIST — Mini-calibrated Taxim render of RTM (sim_tactile_mnist)

9 · Sim Starstruck — Mini-calibrated Taxim render of star objects (sim_starstruck)

10 · UniT — continuous 3D-pose tracking (unit)

11 · TacQuad — quad-sensor benchmark, Mini stream (tacquad)

Investigated but not included

Channel-order normalization

Aggregate statistics

Citation chain

1 · FoTA — Foundation Tactile (`fota_labeled` + `fota_unlabeled`)

2 · py3DCal — sphere-indentation calibration grid (`threedcal`)

3 · FEATS — Force Estimation for Tactile Sensors (`feats`)

4 · GelSLAM — tactile SLAM tracking + reconstruction (`gelslam`)

5 · TactileTracking / NormalFlow — 6DoF pose tracking (`tactile_tracking`)

6 · Real Tactile MNIST — 3D-printed digit touches (`real_tactile_mnist`)

Why we don't use the authors' `touch-real-single-t256-320x240` directly

7 · FeelAnyForce — force-controlled indentations (`feelanyforce`)

8 · Sim Tactile MNIST — Mini-calibrated Taxim render of RTM (`sim_tactile_mnist`)

9 · Sim Starstruck — Mini-calibrated Taxim render of star objects (`sim_starstruck`)

10 · UniT — continuous 3D-pose tracking (`unit`)

11 · TacQuad — quad-sensor benchmark, Mini stream (`tacquad`)