--- license: cc-by-nc-4.0 task_categories: - robotics language: - en tags: - vitra - mano - hand-object-interaction - tactile - egocentric --- # tachin-annotations-v1 **Annotations only — no images, no video.** VITRA-style hand episodes for the **Tachin tactile glove** dataset, with per-hand instructions, paraphrases, and **dense per-frame tactile**. | | | |---|---| | episodes | **2,669** | | training samples (`index_frame_pair` rows) | **79,584** | | annotation | MANO pose + world/camera joints + per-frame extrinsics | | **tactile** | **880 taxels per hand**, plus x/y shear, contact state, and 5 fingertip channels | | text | one instruction per episode + 1.92 paraphrases on average | | source frame rate | 30 fps | | recordings | 102 task segments | | **images / video** | **not included** — see *Getting the frames* below | This is the **densest tactile** in the collection: 880 taxels per hand against EgoTouch's 16×16 = 256 per palm, and it additionally ships shear (`tactile_tf_x` / `tactile_tf_y`), a per-taxel contact state, and 5 fingertip pressure and dynamics channels. ### What we did, and what we took from upstream **The pose is not ours.** The upstream release `MIT-Media-Lab/tachin-vitra-precut-mano-v1` did the hard part: the source ships 21-joint hand tracks and **no MANO**, so MANO was fitted there (per-recording/hand `beta`, per-frame pose, wrist `J0` translation, with a per-frame `fit_mpjpe_m`), the ~55.6 Hz glove streams were interpolated onto the 30 Hz RGB timestamps without extrapolating across gaps wider than 100 ms, and a 12.4-second disagreement between two published RGB clock origins was resolved with the per-frame NTP table rather than a blanket offset. We adopted all of that unchanged. **The episodes and the text are ours.** We re-cut the continuous recordings with VITRA's method: gaussian smooth (sigma=1.0) -> local speed minima in a fixed window (win=15, i.e. 0.5 s at 30 Hz) -> merge runs shorter than min_seg=16 -> pad 2 frames on each end Left and right hands are cut independently. The validity mask is `post_mano_cut_valid_frames`, the strictest of the four the source ships (median 0.955; the others are `source_valid_frames` 0.968, `mano_fit_valid_frames` 0.967, `projection_valid_frames` 0.967 — none is constant, so all four encode real validity). **Instructions are ours.** Two rounds, both with `Qwen3.5-122B-A10B-FP8`: round 1 captions 8 frames per episode with the palm's future trajectory drawn on them; round 2 checks the sentence belongs to that hand, strips same-hand references, and writes 1-3 paraphrases. **Episodes with no instruction are not included** — round 1 returned `N/A` on 597 of 3,019 segments (19.8%); those are excluded from both the archive and the index. Hands are balanced: 1,019 left / 1,403 right, i.e. left is 42% of episodes. ### Coordinates — no SLAM or anchor search needed Per-frame `extrinsics` (World2Cam, OpenCV, metres) ship with every episode and one stable world frame per recording. Measured: the camera really moves (max displacement median 0.196 m, trajectory length 2–12 m), rotations are legal (orthogonality deviation ≤ 1.3e-07), and `joints_camspace == extrinsics · joints_worldspace` to 8e-08 m. Anchor-camera normalisation is **not** baked in; the training dataloader does chunk-level anchor synchronisation, the same as every other domain here. ### Train / val / test split `test` owns whole videos — its 5 videos appear in neither `train` nor `val`. `train` and `val` share the remaining 97 videos and are separated at the episode level. Balanced on **frames**. | split | videos | episodes | frames | share | |---|---:|---:|---:|---:| | train | 97 | 2,159 | 86,872 | 88.97% | | val | 58 | 120 | 4,824 | 4.94% | | test | 5 (exclusive) | 143 | 5,942 | 6.09% | The test share overshoots 5% because there are only 102 videos and `test` must take whole ones; the granularity is limited by video size, not by a bug. ### Files ``` tachin.tar -> Annotation/tachin/episodic_annotations/*.npy episode_frame_index.npz index_frame_pair (N,2) uint32 + index_to_episode_id (E,) splits/{train,val,test}.txt episode ids splits/test_videos.txt the 5 videos test owns splits/meta.json parameters + achieved shares + self-checks ``` ```python import numpy as np # tar -xf tachin.tar z = np.load("episode_frame_index.npz", allow_pickle=True) ep_slot, frame_id = z["index_frame_pair"][sample_id] eid = str(z["index_to_episode_id"][ep_slot]) d = np.load(f"Annotation/tachin/episodic_annotations/{eid}.npy", allow_pickle=True).item() rgb_frame_id = int(d["video_decode_frame"][frame_id]) hand = str(d["anno_type"]) tactile = d["tactile"][hand] # (T, 880) float valid = d["tactile_valid"][hand] # (T, 880) — NaNs are masked here, not filled ``` Each `.npy` also carries `tactile_tf_x` / `tactile_tf_y` (shear), `tactile_contact_state`, `tactile_fingertip_pressure` and `tactile_fingertip_dynamics` (T,5), the static `tactile_sensor_keys` (15) and `tactile_sensor_offsets` (15,2), a `quality` dict of per-frame masks, and the usual `left`/`right` pose dicts. `text[hand] = [(sentence, (0, T))]`, `text_rephrase[hand] = [([paraphrases...], (0, T))]`. ### Getting the frames `video_decode_frame` indexes the **source** video, which we do not redistribute. The segment videos are in `MIT-Media-Lab/tachin-vitra-precut-mano-v1` under `Video/Tachin_root//task-*.mp4`, and `video_name` in each episode is already the full relative path from that repo root — join it directly, do not match by basename. The original dataset is `Tachintech/TachinTactileGlove-Demo01` (revision `2eb0e45e6d2103f33a7329c657a161c067d516c9`). ### Known limitations - Paraphrase count averages 1.92, not a fixed number. - **Small.** 2,422 episodes / 54 minutes of source video — the smallest domain in this collection. Its value is tactile density, not scale. - MANO here is a **fit to 21-joint tracks**, not a multi-view solve; `fit_mpjpe_m` is stored per frame so the fit quality can be filtered on. - Intrinsics are non-square (`fx` 626.9 vs `fy` 578.1) and imply (2cx, 2cy) = 1926.7 × 1069.3 while the video is 1920 × 1080. Use the video's native size. - Verified: the index lists exactly the episodes that have an instruction, every episode's stored frame count matches its index rows, and no index entry points at a missing episode. --- ## Revision — 2026-09-08 (re-cut) **This release replaces the previous one. The previous episodes contained invalid frames and should not be used.** A defect in our episode-cutting step let frames with `kept_frames == False` (invalid hand pose — all-zero or NaN wrist coordinates) stay inside published episodes. The validity mask was only used to keep a cut point from landing on an invalid frame; it did not constrain what a segment contained. Worse, a run of invalid frames could suppress cutting altogether, so the gap was swallowed into one long segment instead of being excluded. Every episode here is now built from a run of consecutive valid frames, so `kept_frames` is all-`True` by construction — verified over the whole collection: **568,369 episodes / 16.5 M frames, zero `kept_frames == False`**. Both instruction rounds were regenerated for the new segmentation. Segment counts and episode ids therefore changed, and the index and splits were rebuilt: | | previous | this release | |---|---:|---:| | episodes on disk | 3,019 | 3,420 | | episodes published (with an instruction) | 2,422 | 2,669 | | training samples | 97,638 | 79,584 | Splits are `video_test` (test owns whole videos disjoint from train/val; train and val share the remaining videos and are split at the episode level), balanced on **frames** at 90/5/5, seed 1. | dataset | episodes | training samples | our contribution | size | HF | |---|---:|---:|---|---:|---| | **EPIC-KITCHENS-100** | 149,570 | 4,019,534 | episodes + text | 8.70 GB | [`epic30-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/epic30-annotations-v1) | | **EgoTouch** | 107,364 | 3,123,675 | episodes + text + **tactile** | 17.02 GB | [`egotouch-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/egotouch-annotations-v1) | | **GigaHands** | 70,486 | 2,266,087 | episodes + text | 2.89 GB | [`gigahands-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/gigahands-annotations-v1) | | **Ego-Exo4D** | 67,051 | 1,757,474 | text only | 4.09 GB | [`egoexo4d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/egoexo4d-annotations-v1) | | **OakInk2** | 29,058 | 1,052,924 | episodes + text | 1.56 GB | [`oakink2-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/oakink2-annotations-v1) | | **TACO** | 23,757 | 736,136 | episodes + text | 1.34 GB | [`taco-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/taco-annotations-v1) | | **HOT3D** | 18,805 | 619,680 | episodes + text | 1.51 GB | [`hot3d-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/hot3d-annotations-v1) | | **ARCTIC** | 12,610 | 425,796 | episodes + text | 0.85 GB | [`arctic-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/arctic-annotations-v1) | | **H2O** | 5,845 | 200,332 | episodes + text | 0.40 GB | [`h2o-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/h2o-annotations-v1) | | **Tachin** | 2,669 | 79,584 | episodes + text + **tactile** | 3.38 GB | [`tachin-annotations-v1`](https://huggingface.co/datasets/MIT-Media-Lab/tachin-annotations-v1) | | **total** | **487,215** | **14,281,222** | | **41.7 GB** | | > **Something-Something V2 was dropped** from the collection (12 fps against > 30 fps everywhere else, so a 16-step action chunk spans 1.33 s instead of 0.53 s). > The repository still exists but should not be used.