FORMAT.md

HTT dataset

Paired multi-sensor tactile data for the Heterogeneous Tactile Transformer (HTT). Four sensors — two vision-based (GelSight Mini, 9DTact) and two taxel arrays (Xela uSkin, TAC-02) — organized into four task splits.

sensor	type	raw format
gsmini	vision (GelSight Mini)	JPEG [224, 224, 3]
9dtact	vision (9DTact)	JPEG [224, 224, 3]
xela	taxel array (Xela uSkin)	float [T, 72]
tac02	taxel array (TAC-02)	float [T, 66]

├── pretrain/        1. self-supervised pretraining (paired episodes, no labels)
├── classification/  2. 20-object classification (paired episodes, labeled)
├── force/           3. 6D force estimation (per-sensor static probe episodes)
├── slip/            4. slip-stage detection (per-sensor sliding episodes)
├── bg_data/         background/reference frames per sensor
├── STATS.json       measured per-split statistics (machine-readable)
└── MANIFEST.sha256  checksums for every file

Dataset statistics — 1.59M frames

One paired sample = 1 optical frame (224×224 RGB) + 1 synchronized chunk of 10 consecutive taxel frames. The npz splits are counted as force-aligned timesteps (taxel sensors at native rate, vision sensors at camera rate).

corpus	episodes	frames (native rate)
pretrain (paired, both pairs)	1,789	710,633 (64,603 optical + 646,030 array)
classification (paired, both pairs)	599	239,184 (21,744 optical + 217,440 array)
force task: static + sliding modes	401 + 405	423,284 (static 209,869 + sliding 213,415)
slip task: sliding modes	405	213,415
frame–task instances		1,586,516 ≈ 1.59M
distinct physical frames		1,373,101

Sliding-mode episodes carry both 6D force and slip labels, so they serve two tasks; the frame–task total counts them twice, the distinct total once.

Note on the force task protocol: the sliding episodes under slip/ also contain time-aligned 6D force and can be used for force training (mode_filter=None in the loaders reads both modes from a merged root). All force results reported in the HTT paper use the static mode only (mode_filter: static), i.e. exactly the force/ split; slip results use exactly the slip/ split.

Per-sensor force-aligned frame counts:

	xela	tac02	9dtact	gsmini
force (static)	105,691	54,718	24,829	24,631
slip (sliding)	108,649	54,717	24,900	25,149

1. pretrain/ — paired episodes, label-free

WebDataset tar shards for two sensor pairs, collected with the sensor pair touching the same object simultaneously:

• pretrain/xela_9dtact/pretrain_{train,val,test}_{0000..0003}.tar
• pretrain/tacniq_gsmini/pretrain_{train,val,test}_{0000..0003}.tar (tacniq is the TAC-02 sensor)

Each episode contributes, with a shared basename prefix:

episode_N.bg.jpg              background frame (vision sensor)
episode_N_XXXXXX.<vis>.jpg    vision frame XXXXXX
episode_N_XXXXXX.<tax>.npy    taxel chunk XXXXXX  [frames_per_chunk, D]
episode_N_meta.json           structural metadata only
_manifest.json                per-shard episode list + shard info

This split is fully unlabeled. All object and action annotations have been removed from both episode_N_meta.json and _manifest.json; the metadata retains only structural fields (episode id, frame counts, chunking). The removal is audited programmatically: no label key and no label vocabulary string appears anywhere in these shards, and the pretrain episode ids are disjoint from the classification episode ids, so object identity cannot be recovered by cross-referencing the labeled split.

2. classification/ — 20-object supervised episodes

Same episode format and pairs as pretrain/ (classification/<pair>/supervised_{train,val,test}_{0000..0003}.tar), with object (20 classes) and action (press / slide / twist) kept in episode_N_meta.json and _manifest.json.

3. force/ — 6D force estimation

force/<sensor>/processed/p{1..4}_static/*.npz — static pressing episodes for 4 probe tips, 50 episodes each, with time-aligned ATI force-torque readings.

Vision npz keys: ref_frame [224,224,3], ref_force [6], tactile_img [T,224,224,3] uint8, 6d_force [T,6], probe, mode. Taxel npz keys: ref_tactile [D] and tactile [T,D] instead of images.

4. slip/ — slip-stage detection

slip/<sensor>/processed/p{1..4}_sliding/*.npz — sliding episodes in the same npz format as force/ — plus slip/<sensor>/sliding_labeled/p{1..4}_sliding/*.labeled.npz with per-frame 3-class labels (sliding_labels_bracket: 0 static / 1 incipient / 2 gross) and friction diagnostics (mus, fz, c_plus, c_minus). A label file maps to its episode by path convention: processed/pK_sliding/foo.npz ↔ sliding_labeled/pK_sliding/foo.labeled.npz.

Loading

The HTT code release ships dataloaders for every split; point them at this directory:

• pretrain / classification: data/xela_9dtact_dataloader_webdataset.py, data/tacniq_gsmini_dataloader_webdataset.py (data_root = the pair dir, dataset_split_type = pretrain | supervised)
• force / slip: data/taxel_force_4probe_50each_dataloader.py, data/gsmini_force_4probe_50each_dataloader.py (data_root = <split>/<sensor>/processed, mode_filter = static | sliding)

Integrity

sha256sum -c MANIFEST.sha256