Add files using upload-large-folder tool

FORMAT.md

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+# 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
+
+```bash
+sha256sum -c MANIFEST.sha256
+```

README.md

@@ -0,0 +1,72 @@
+---
+license: cc-by-nc-4.0
+pretty_name: HTT dataset
+tags:
+- robotics
+- tactile-sensing
+- multimodal
+size_categories:
+- 1M<n<10M
+---
+
+# HTT dataset
+
+Paired multi-sensor tactile data (**1.59M frames**) for the Heterogeneous
+Tactile Transformer (HTT). Four sensors — two vision-based, two taxel arrays —
+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, fully unlabeled)
+├── classification/  2. 20-object classification (paired episodes, labeled)
+├── force/           3. 6D force estimation (static probe episodes, aligned F/T)
+├── slip/            4. slip-stage detection (sliding episodes, 3-class labels)
+├── bg_data/         background/reference frames per sensor
+├── STATS.json       measured per-split statistics
+├── FORMAT.md        detailed per-split format documentation
+└── MANIFEST.sha256  checksums for every file
+```
+
+## Statistics
+
+One *paired sample* = 1 optical frame + 1 synchronized chunk of 10 taxel
+frames; npz splits are counted as force-aligned timesteps at native sensor
+rate.
+
+| corpus | episodes | frames |
+|---|---:|---:|
+| 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 |
+| **total** | | **≈ 1.59M** |
+
+Notes:
+- The `pretrain/` split is **fully unlabeled**: all object/action annotations
+  are removed from the shards, and pretrain episodes are disjoint from the
+  labeled classification episodes.
+- Sliding episodes carry time-aligned 6D force as well, so they can also
+  serve force training; **all force results in the HTT paper use static mode
+  only** (the `force/` split), and slip results use the `slip/` split.
+
+## Download
+
+```bash
+hf download AllenBi21/HTT-dataset --repo-type dataset --local-dir HTT-dataset
+cd HTT-dataset && sha256sum -c MANIFEST.sha256   # optional integrity check
+```
+
+Dataloaders for every split ship with the HTT code release
+([github.com/jxbi1010/HTT](https://github.com/jxbi1010/HTT)); pretrained
+weights: [AllenBi21/HTT](https://huggingface.co/AllenBi21/HTT). Formats are
+documented in [`FORMAT.md`](FORMAT.md).
+
+## License
+
+CC-BY-NC-4.0 (non-commercial research use).

STATS.json

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+{
+  "counting": "1 paired sample = 1 optical frame + 1 chunk of 10 taxel frames; npz splits counted as 6d_force-aligned timesteps",
+  "pretrain": {
+    "per_pair_split": {
+      "tacniq_gsmini/test": {
+        "episodes": 100,
+        "paired": 4839,
+        "bg": 100
+      },
+      "tacniq_gsmini/train": {
+        "episodes": 720,
+        "paired": 34881,
+        "bg": 720
+      },
+      "tacniq_gsmini/val": {
+        "episodes": 80,
+        "paired": 3841,
+        "bg": 80
+      },
+      "xela_9dtact/test": {
+        "episodes": 99,
+        "paired": 2358,
+        "bg": 99
+      },
+      "xela_9dtact/train": {
+        "episodes": 711,
+        "paired": 16819,
+        "bg": 711
+      },
+      "xela_9dtact/val": {
+        "episodes": 79,
+        "paired": 1865,
+        "bg": 79
+      }
+    },
+    "paired_samples": 64603,
+    "optical_frames": 64603,
+    "array_frames": 646030,
+    "raw_frames": 710633,
+    "episodes": 1789,
+    "bg_frames": 1789
+  },
+  "classification": {
+    "per_pair_split": {
+      "tacniq_gsmini/test": {
+        "episodes": 40,
+        "paired": 1964,
+        "bg": 40
+      },
+      "tacniq_gsmini/train": {
+        "episodes": 239,
+        "paired": 11731,
+        "bg": 239
+      },
+      "tacniq_gsmini/val": {
+        "episodes": 20,
+        "paired": 958,
+        "bg": 20
+      },
+      "xela_9dtact/test": {
+        "episodes": 40,
+        "paired": 942,
+        "bg": 40
+      },
+      "xela_9dtact/train": {
+        "episodes": 240,
+        "paired": 5672,
+        "bg": 240
+      },
+      "xela_9dtact/val": {
+        "episodes": 20,
+        "paired": 477,
+        "bg": 20
+      }
+    },
+    "paired_samples": 21744,
+    "optical_frames": 21744,
+    "array_frames": 217440,
+    "raw_frames": 239184,
+    "episodes": 599,
+    "bg_frames": 599
+  },
+  "force": {
+    "per_sensor": {
+      "xela": {
+        "episodes": 100,
+        "frames": 105691
+      },
+      "tac02": {
+        "episodes": 100,
+        "frames": 54718
+      },
+      "9dtact": {
+        "episodes": 100,
+        "frames": 24829
+      },
+      "gsmini": {
+        "episodes": 101,
+        "frames": 24631
+      }
+    },
+    "frames": 209869,
+    "episodes": 401
+  },
+  "slip": {
+    "per_sensor": {
+      "xela": {
+        "episodes": 104,
+        "frames": 108649
+      },
+      "tac02": {
+        "episodes": 100,
+        "frames": 54717
+      },
+      "9dtact": {
+        "episodes": 100,
+        "frames": 24900
+      },
+      "gsmini": {
+        "episodes": 101,
+        "frames": 25149
+      }
+    },
+    "frames": 213415,
+    "episodes": 405
+  },
+  "totals": {
+    "paired_corpora_raw_frames": 949817,
+    "force_task_frames_static_plus_sliding": 423284,
+    "force_task_frames_static_only_paper_protocol": 209869,
+    "slip_task_frames": 213415,
+    "frame_task_instances": 1586516,
+    "distinct_frames": 1373101
+  }
+}

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