--- license: cc-by-nc-4.0 pretty_name: TouchScale language: - en task_categories: - robotics - video-classification tags: - egocentric - tactile - visuo-tactile - haptics - manipulation - bimanual - rgb-d - imu - embodied-ai - webdataset size_categories: - 10KTouchScale

Egocentric Human Vision and Touch for Visual–Tactile Learning

> [!NOTE] > **Release status:** this repository currently hosts **100 hours** of TouchScale. The full **500-hour** dataset is planned for public release before November 2026.

## Overview **TouchScale** captures how people see *and feel* everyday manipulation. Participants wear a unified capture rig — a head-mounted RGB-D camera, two wrist cameras and full-hand tactile gloves with 880 taxels per hand — and perform short, instructed tasks across laboratory, kitchen, workbench, office, medical, bedroom and packing environments. Every stream is recorded on a shared clock, so each frame of video is paired with dense, bimanual contact measurements. This repository currently hosts **100 hours** of the 500-hour TouchScale dataset: **15,324 episodes**, **929 tasks** and **22 scene types**, each with an English natural-language instruction. The full 500 hours are planned for public release before November 2026. ## Demo

_{Wring out a wet cloth once with both hands. Head RGB, head depth, both wrist views, per-hand total
force and full-hand tactile maps, all time-aligned. Full episode (1080p)}

_{Place gauze and secure it with tape · Medical / First Aid · full episode (1080p)}	_{Seal a carton with a tape dispenser · Parcel / Packing · full episode (1080p)}
_{Pour water into a spray bottle · Household · full episode (1080p)}	_{Pour from a lab scoop into a cylinder · Laboratory · full episode (1080p)}

Every episode also has its own preview video — open the **Dataset Viewer** above (`preview` subset) to browse and play any of the 15,324 episodes alongside its task and instruction. ## Key Features ### 🖐️ Dense bimanual touch Full-hand tactile gloves on both hands with **880 taxels per hand** (1.5 × 1.5 mm cells) over all five fingers and the palm, recording per-taxel normal force. ### 👁️ Egocentric RGB-D with wrist views A head-mounted RGB-D camera captures the whole interaction; two palm-side wrist cameras capture close-up hand–object contact that the head view often cannot see. ### ⏱️ One consistent, synchronized pipeline All recordings use the same generation of hardware and the same synchronization pipeline. Every sample keeps its original timestamps, so streams can be aligned at any rate. ### 📝 Language-annotated tasks Each episode comes with a task name and a step-by-step instruction describing the initial state, the actions (including which hand leads), and the completion state. ### ✅ Quality-controlled Every episode passes automated checks on completeness, frame timing, cross-stream synchronization, video integrity, visual quality and tactile signal validity (see [Quality Control](#quality-control)). ## Dataset at a Glance | | | |---|---| | Episodes | 15,324 | | Duration | 100.0 h in this release (of 500 h; full release planned before November 2026) | | Tasks | 929 | | Scene types | 22 | | Objects | 905+ | | Participants | 24 (pseudonymous ids) | | Episode length | median 21.9 s (5th–95th percentile 11.3–42.6 s) | | Size | ~1.05 TB in 430 WebDataset shards (full) + per-episode previews |

Hours by scene (table)

| Scene | Tasks | Episodes | Hours | |---|---:|---:|---:| | Laboratory | 163.0 | 2,881.0 | 15.0 | | Lab Apparatus & Assembly Workbench | 90.0 | 1,268.0 | 13.8 | | Break-Room Supply & Display Cabinet | 77.0 | 1,494.0 | 12.6 | | Kitchen Prep Workbench | 65.0 | 1,206.0 | 11.1 | | Medical / First Aid | 51.0 | 1,187.0 | 7.5 | | Kitchen | 47.0 | 1,105.0 | 6.0 | | Office | 46.0 | 1,070.0 | 5.9 | | Parcel / Packing | 39.0 | 907.0 | 5.7 | | Tool Bench | 34.0 | 771.0 | 4.5 | | Bedroom | 29.0 | 663.0 | 3.5 | | Test Tube & Beaker Workbench | 17.0 | 398.0 | 2.2 | | Lab Assembly Station | 41.0 | 391.0 | 1.8 | | Fragile Food & Portioning Station | 123.0 | 313.0 | 1.6 | | Layered Materials & Filing Station | 26.0 | 374.0 | 1.6 | | Bedroom / Laundry | 10.0 | 224.0 | 1.5 | | Household Cleaning / Organizing | 8.0 | 186.0 | 1.2 | | Tools & Continuous-Contact Workbench | 11.0 | 261.0 | 1.2 | | Deformable Objects & Tactile Judgment Station | 10.0 | 167.0 | 1.0 | | Kitchen Prep Counter | 18.0 | 164.0 | 0.8 | | Insertion & Press-Fit Workbench | 7.0 | 139.0 | 0.7 | | Break-Room Supply Cabinet | 16.0 | 142.0 | 0.5 | | Lab Bench | 1.0 | 13.0 | 0.1 |

## Sensors and Modalities | Stream | Sensor | File | Rate | Format | |---|---|---|---|---| | Head RGB | Orbbec Gemini 345Lg | `rgb_head.mp4` | 30 fps | H.264, 1280×720 | | Head depth | Orbbec Gemini 345Lg (registered to head RGB) | `depth_head.mkv` | 30 fps | FFV1, 16-bit, millimetres, 1280×720 | | Left / right wrist RGB | Intel RealSense D405 | `wrist_left.mp4`, `wrist_right.mp4` | 30 fps | H.264, 1280×720 | | Tactile gloves | full-hand tactile gloves, 880 taxels/hand | `left_hand_data.npz`, `right_hand_data.npz` | 35–90 Hz (timestamped) | per-pad taxel grids | | Head IMU | Orbbec Gemini 345Lg | `imu.txt` | ~200 Hz | accel (m/s²) + gyro (rad/s) | | Aligned episode | — | `episode.h5` | 30 Hz timeline | timestamps, IMU, tactile, task, calibration | Every video has a per-frame timestamp file (`*.csv`: `frame_index,timestamp_s`, Unix seconds) on the shared clock; wrist and head cameras are aligned to within 0.1 s. Camera intrinsics are in `head_param.json` and a Kalibr-format RGB / depth / IMU calibration in `kalibr_parameters.yaml`. ### Tactile glove layout

Each glove has 15 sensor pads. In the NPZ files, pad `k` is stored as `tactile_` with shape `(T, rows, cols)` (normal force per taxel, newtons), plus `tf_tactile_x_` / `tf_tactile_y_` (tangential components) and `timestamps`. Exact 2-D taxel positions for both hands are provided in `metadata/glove_geometry_left.json` and `metadata/glove_geometry_right.json`. ## Dataset Structure ``` TouchScale/ ├── README.md ├── assets/ # figures and demo videos shown on this card ├── metadata/ │ ├── episodes.parquet # one row per episode (fields below) │ ├── glove_geometry_left.json # taxel layout of the left glove │ └── glove_geometry_right.json ├── previews/train/ # `preview` subset (Dataset Viewer): one preview video per episode │ ├── metadata.parquet │ └── 000/ … 429/*.mp4 └── data/ # full data: all raw streams, WebDataset shards (~2.5 GB each) ├── train-00000-of-00430.tar └── … ``` Inside a shard, all files of an episode share the key ``: ``` .json episode metadata .rgb_head.mp4 .rgb_head.csv .depth_head.mkv .depth_head.csv .wrist_left.mp4 .wrist_left.csv .wrist_right.mp4 .wrist_right.csv .left_hand_data.npz .right_hand_data.npz .glove_left_calib.json .glove_right_calib.json .imu.txt .episode.h5 .head_param.json .kalibr_parameters.yaml ``` ### Episode metadata | Field | Description | |---|---| | `episode_id` | unique episode id | | `task_id` | task identifier (same task → same id) | | `task_name` | short task name | | `scene` | scene / workstation type | | `instruction` | full instruction: initial state, actions, completion state, suggested duration | | `items` | objects involved | | `primary_hand` | leading hand for mirrored task variants (`left` / `right`, null if unspecified) | | `duration_s`, `num_frames` | episode length (head camera) | | `collection_date` | recording date | | `actor_id`, `actor_gender` | pseudonymous participant id and gender | | `glove_rate_hz` | tactile sampling rate of the episode | | `tactile_contact_frac_left` / `_right` | fraction of glove samples with summed normal force > 3 N | | `head_gyro_p95_rad_s` | 95th-percentile head angular speed | | `quality_score` | quality score in [0, 1] from the QC pipeline | | `shard` | tar shard containing the episode | ### `episode.h5` ``` metadata/ attrs: schema_name, schema_version, nominal_fps, n_hands, ... time/timestamp_ns (N,) 30 Hz episode timeline obs/video// epoch (N,), src_idx (N,) frame index into each video per timeline step obs/imu/ epoch, accel (M,3), gyro (M,3) obs/tactile/raw// t_epoch, pad_, shear_x_, shear_y_ (trimmed to the episode) task/ name, task_id, steps, success, seg_start, seg_end calibration/ head_param, kalibr, glove_left, glove_right ``` ## Usage Browse the previews (the default subset). Install `torchcodec` to decode the videos, or call `.decode(False)` to get file references only: ```python from datasets import load_dataset previews = load_dataset("2077AIDataFoundation/TouchScale", split="train", streaming=True) row = next(iter(previews.decode(False))) print(row["task_name"], "-", row["instruction"]) print(row["video"]["path"]) ``` Stream the full data with the WebDataset loader (no full download needed): ```python from datasets import load_dataset ds = load_dataset("webdataset", data_files={"train": "hf://datasets/2077AIDataFoundation/TouchScale/data/train-*.tar"}, split="train", streaming=True).decode(False) ep = next(iter(ds)) meta = ep["json"] # episode metadata (dict) glove = ep["right_hand_data.npz"] # dict of arrays: timestamps, tactile_, ... open("rgb_head.mp4", "wb").write(ep["rgb_head.mp4"]["bytes"]) open("episode.h5", "wb").write(ep["episode.h5"]["bytes"]) ``` Select episodes by metadata, then read only the shards you need with `webdataset`: ```python import io, numpy as np, pandas as pd, webdataset as wds from huggingface_hub import hf_hub_download meta = pd.read_parquet(hf_hub_download("2077AIDataFoundation/TouchScale", "metadata/episodes.parquet", repo_type="dataset")) shards = sorted(meta[meta.scene == "Kitchen"].shard.unique()) urls = [f"https://huggingface.co/datasets/2077AIDataFoundation/TouchScale/resolve/main/{s}" for s in shards] for ep in wds.WebDataset(urls): right = np.load(io.BytesIO(ep["right_hand_data.npz"])) fingertip = right["tactile_3"] # index fingertip, (T, 9, 8) normal force in N t = right["timestamps"] # Unix seconds, same clock as the videos ``` Decode depth (16-bit, millimetres): ```python import subprocess, numpy as np raw = subprocess.run(["ffmpeg", "-v", "error", "-i", "depth_head.mkv", "-f", "rawvideo", "-pix_fmt", "gray16le", "-"], capture_output=True).stdout depth_mm = np.frombuffer(raw, np.uint16).reshape(-1, 720, 1280) ``` ## Quality Control Every released episode passes all of the following automated checks: - **Completeness** — all video, depth, tactile, IMU and calibration files present and non-empty; 5 s ≤ duration ≤ 120 s. - **Timing** — per stream ≤ 2 % dropped frames, no gap > 0.25 s, monotonic timestamps; wrist streams within 0.1 s of the head camera; IMU 180–220 Hz without gaps. - **Video integrity** — container frame counts match the timestamp logs; no truncated, dark or covered camera streams. - **Visual quality** — head video sampled at 2 fps: no substantial dark, over-exposed, frozen or blurry segments. - **Tactile validity** — no missing values, no sampling gaps > 0.25 s, and measurable contact (> 3 N) during the task. ## Limitations - Participants are predominantly male (95 of 15,324 episodes are by female participants). - The tactile sampling rate differs between recording sessions (about 35, 57 or 65–90 Hz); always use the per-sample timestamps. - Tactile values are the glove's calibrated output in newtons. - All episodes are successful demonstrations; there are no failure examples. ## License TouchScale is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/): free for non-commercial research and education with attribution. For commercial use, please contact 2077AI. ## Citation If you use TouchScale, please cite: ```bibtex @article{li2026touchscale, title = {TouchScale: 500 Hours of Human Vision and Touch for Visual-Tactile Learning}, author = {Li, Dayou and Wang, Hao and Yang, Qianqian and Zhu, Zihao and Fang, Haoquan and Zeng, Ziyao and Han, Yan and Wang, Zihan and Wang, Yan and Huang, Baoru and Wang, Dilin and Shimada, Kenji and Luo, Yiyue and Li, Manling and Lv, Teresa and Mukadam, Mustafa and Ranjan, Rakesh and Zhang, Ruohan and He, Qi and Liu, Changliu and Chen, Xu and Pavone, Marco and Liu, Bangya and Li, Jiachen and Tomizuka, Masayoshi and Fan, Zhiwen}, journal = {arXiv preprint arXiv:2610.10288}, year = {2026} } ```