Datasets:
SoTac — SO-101 Tactile Manipulation Corpus
SoTac is a growing corpus of visuo-tactile teleoperation demonstrations recorded on an SO-101 arm with dual 3-axis tactile fingertip sensors. Every 30 Hz observation carries a full per-taxel force map for both fingertips, and every episode ships with the raw ~91 Hz tactile stream as a sidecar — no downsampling at record time.
Created using LeRobot (via the lerobotac sensor fork).
Use the button above — it opens the LeRobotAC tactile visualizer with per-taxel 3D force arrows, contact timelines, and the raw 91 Hz stream panels. The standard LeRobot viewer does not render the tactile data.
Contents
SoTac is actively growing — new tasks, objects, and sensors will be added over time.
This is the curated set. Episodes 0–62 were fully human-reviewed (trim, outcome, tactile events).
Episodes 21–70 and 113–162 (added 2026-09-02 from raw episodes 77–176, collected by Jingming Zhang) are trimmed with
reviewer-approved cut points; their outcome and tactile-event annotations are still pending and they carry
reviewed: false in episode_annotations.json. Episodes with contact
faults (accidental gripper contact during approach, closed-finger approaches,
post-release contact), operator visibility, or sensor faults are removed, and idle
heads/tails are trimmed so every episode starts near task onset. The unedited archive
is Jingyi-Z/sotac_raw.
Current release (2026-09-02): 163 episodes / 55,457 frames (~31 min) across 4 pick-and-place tasks, grouped by task:
| task | episodes | episode # |
|---|---|---|
| Pick up the red foam ball and place it into the container | 71 | 0–70 |
| Pick up the orange rubber ball and place it into the container | 22 | 71–92 |
| Pick up the red cup and place it on top of the container | 20 | 93–112 |
| Pick up the green rubber ball and place it into the container | 50 | 113–162 |
Per-episode annotations ship in annotations/:
episode_annotations.json: episode-level curation labels — grasp type, attempt count, outcome (success / failure / partial), distractor objects (11 episodes include an orange ball or white box as distraction), grip-force quality labels (insufficient/excessive force), and free-form event notes.episode_XXXXXX.json(the 63 fully reviewed episodes so far): time-aligned language annotations — subtask segments (approach, grasp, transport, place_release) and tactile event interjections (contact, grasp_stable, slip, rotation, place, release/drop) with per-event confidence. Auto-generated from the 91 Hz tactile stream and human-reviewed in the visualizer.
Stable provenance: every episode carries a source_raw_episode field — both as a
column in meta/episodes and in the annotations — pointing to its index in
sotac_raw, which is append-only
and never renumbered. Curated releases may renumber freely; raw indices are the
permanent episode identity. curation_map.json in the root holds the full mapping.
Grip forces span gentle (4–10 N peak) to firm (50 N peak) handling across sessions.
Data collection: episodes 0–5, 21–70, 93–97, 113–162 by Jingming Zhang, 6–20, 84–92, 98–112 by Jingyi Zou, 71–83 by Yuzhou Wang.
Curation provenance: derived from sotac_raw with
curate_tactile_dataset.py
and the spec curation_sotac_v2.json (same repo) — all deletions and per-episode
keep-windows are reproducible.
Hardware
- Robot: SO-101 follower arm, teleoperated with an SO-101 leader arm
- Cameras: wrist + top, 640×480 @ 30 fps (MJPG)
- Tactile: 2× Paxini PX-6AX GEN3 (DP-S2015-Elite) fingertip pads on a shared
High-Speed Communication Board — 52 taxels per pad, 3-axis force per taxel,
0.1 N/LSB, ~91 Hz effective rate for both pads together. Firmware zeroing at
connect. Finger
[0]= gripper-side pad (module 10), finger[1]= wrist-roll-side pad (module 18).
Data format
Standard LeRobot v3.0 dataset, plus one extra feature and one extra directory:
observation.sensors.paxini_fingertip — float32, shape (2, 52, 3):
the latest raw tactile sample per finger at each 30 Hz observation
(fingers × taxels × [fx, fy, fz], in units of 0.1 N). Per-taxel (x, y, z) mm
coordinates for the GEN3 layout are available in
paxini-sdk.
sensors/paxini_fingertip/episode_XXXXXX/ — raw ~91 Hz stream sidecars:
sensor_1.csv,sensor_2.csv(fingers 0 and 1): 163 columns —timestamp_ns, frame_status, time_calibration_offset_ns, calibrated_timestamp_ns, fx, fy, fz, p_00_fx … p_51_fz(resultant force followed by 52 per-taxel triplets)alignment.json: episode-start epoch timestamp (time.time_ns) anchoring the raw stream to frame 0 of the 30 Hz main table
Collection protocol
Episodes start with the gripper open and the pads contact-free (calibration reference). The operator and leader arm are kept out of both camera views where possible; remaining appearances are flagged per-episode in the annotations. Object placement is varied between episodes; some episodes include distractor objects.
Tooling
- Recording: lerobotac (
hall-sensorbranch) — LeRobot fork with a sensor framework; thepaxinisensor'scombinedoutput mode +record_raw_csvproduced this dataset - Sensor SDK: paxini-sdk
- Visualization / annotation: LeRobotAC Dataset Visualizer
Dataset Structure
{
"codebase_version": "v3.0",
"fps": 30,
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6
]
},
"observation.state": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos"
],
"shape": [
6
]
},
"observation.images.wrist": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.images.top": {
"dtype": "video",
"shape": [
480,
640,
3
],
"names": [
"height",
"width",
"channels"
],
"info": {
"video.height": 480,
"video.width": 640,
"video.codec": "av1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"video.fps": 30,
"video.channels": 3,
"has_audio": false
}
},
"observation.sensors.paxini_fingertip": {
"dtype": "float32",
"shape": [
2,
52,
3
],
"names": [
"dim_0",
"dim_1",
"dim_2"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
}
},
"total_episodes": 163,
"total_frames": 55457,
"total_tasks": 4,
"chunks_size": 1000,
"data_files_size_in_mb": 100,
"video_files_size_in_mb": 200,
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4",
"robot_type": "so_sensor_follower",
"splits": {
"train": "0:163"
}
}
Citation
@misc{zou2026sotac,
author = {Zou, Jingyi},
title = {SoTac: An SO-101 Tactile Manipulation Corpus},
year = {2026},
howpublished = {\url{https://huggingface.co/datasets/Jingyi-Z/sotac}}
}
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