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Pico Egocentric Dataset · Basic Edition

First-person stereo video, depth and head pose of real shelf-work and facility tasks, captured on a Pico VR headset.

By OpenElephant Intelligence (公象智能), Beijing.

This is the free, open entry point to a much larger commercial collection (10,000+ hours). It is meant for evaluating the data quality and format before requesting the Advanced Edition or a commercial license.

At a glance


Episodes	1,203 across 21 tasks in 2 environments
Device	Pico VR headset, head-mounted, operator's natural viewpoint
Video	Stereo RGB, 1920 × 1456, 30 fps, undistorted (H.264 MP4)
Depth	Per-frame depth from the headset depth sensor (320 × 240)
Head pose	6-DoF headset pose at ~1 kHz
Audio	Stereo AAC + 4-channel Opus
Calibration	Per-episode intrinsics, camera-to-IMU extrinsics, stereo baseline (~64 mm)
License	CC BY-NC 4.0 (commercial license available)

Editions

Edition	Contents	Access
Basic (this repo)	Stereo video + depth + 6-DoF head pose + audio + calibration	🟢 Instant approval
Advanced	Everything in Basic + pose-aligned point clouds + MCAP recordings (Foxglove) + time-stamped action captions	🔒 Reviewed
Commercial	10,000+ hours, MANO hand reconstruction, action segmentation with skill labels, LeRobot export, custom collection	✉️ jiuchen@openelephant.ai

Environments and tasks

`hengxuan-commercial-bank-retail-area` — retail shelves · 879 episodes

Real store shelving with beverage coolers, snack gondolas and hanging snack racks. Most tasks are "error correction": the shelf starts out mis-stocked and the operator finds and fixes the misplaced items.

Task folder	What the operator does	Episodes
`supplement-hanging-melon-seed-snacks`	Restock a hanging rack of melon-seed snacks	145
`organize-and-replenish-snacks`	Tidy and restock a snack shelf	126
`snack-correction-mixed-pickled-mustard-and-kelp-shreds`	Separate mixed pickled-mustard and kelp-shred packs	121
`correction-of-shelf-snacks-different-categories-of-duck-products-are-mixed`	Sort mixed duck-snack products back to their slots	120
`整理袋-桶装薯片`	Arrange bagged and canned potato chips	103
`correcting-shelf-snacks-mixing-different-categories-of-meatballs`	Sort mixed meatball-snack categories	81
`correcting-shelf-snacks-mixing-different-types-of-pickled-mustard`	Sort mixed pickled-mustard varieties	58
`beverage-error-correction-mixing-different-tea-drinks`	Sort mixed tea drinks in a cooler	45
`correction-of-hanging-snacks-mixed-peanut-flavors`	Sort hanging peanut snacks by flavor	23
`correction-of-hanging-snacks-mixed-hanging-of-different-brands-of-melon-seeds`	Sort hanging melon seeds by brand	20
`beverage-error-correction-mixing-different-brands-of-beer`	Sort mixed beer brands	11
`beverage-error-correction-mixing-different-brands-of-soda-water`	Sort mixed soda-water brands	10
`clean-up-and-replenish-mineral-water`	Clear and restock bottled water	9
`organize-hanging-melon-seed-snacks`	Tidy a hanging melon-seed rack	5
`correction-of-hanging-snacks-mixed-rice-crackers-with-different-flavors`	Sort hanging rice crackers by flavor	1
`snack-correction-mixing-different-types-of-pickled-mustard`	Sort mixed pickled-mustard varieties	1

`一楼体育馆自采` — indoor gymnasium · 324 episodes

A large indoor sports hall with fitness equipment, mats and event cabling.

Task folder	What the operator does	Episodes
`整理收拾一半的电线`	Finish coiling partially tidied cables	107
`收纳瑜伽砖`	Collect and stack yoga blocks	84
`整理电线`	Coil and tidy loose cables	78
`整理收纳瑜伽垫`	Roll up and store yoga mats	39
`摆好普拉提圈`	Lay out Pilates rings	16

File layout

<environment>/<task>/case_<id>/
├── head_left_camera_undistorted.mp4     # left RGB, 1920×1456 @ 30 fps, undistorted
├── head_right_camera_undistorted.mp4    # right RGB, same
├── video_index.json                     # per-frame timestamps for both videos
├── undistort_camera.json                # original + rectified intrinsics (K, D)
├── spatial_calibration.json             # intrinsics, extrinsics, depth format, time base
├── video_stream_depth_frames.npz        # depth frames
├── video_stream_head_pose_6dof.npz      # 6-DoF head pose, ~1 kHz
├── video_stream_audio_aac_stereo.m4a    # stereo audio
└── video_stream_audio_opus_4ch.ogg      # 4-channel audio

Earlier captures. 218 retail episodes recorded with an earlier pipeline (case IDs starting 15171–15190) contain the two videos, video_index.json and undistort_camera.json only — no depth, head pose, audio or spatial_calibration.json. All gymnasium episodes and all later retail episodes include every file above.

Calibration

spatial_calibration.json gives, for the left and right RGB cameras and the depth camera:

intrinsics (fx, fy, cx, cy) and distortion (equiDis62 fisheye for RGB, Brown for depth)
extrinsics_4x4: T_IMU←camera, mapping the OpenCV camera frame to the OpenXR head / IMU frame
depth.format: encoding of the depth values
timebase_us: common time base (µs) shared by RGB, depth and pose streams
baseline_mm: stereo baseline

undistort_camera.json holds the original (K_original, D) and rectified (K_new) intrinsics used to produce the undistorted videos. Use K_new with the MP4 files.

Quick start

from huggingface_hub import snapshot_download

# Download one task (roughly 0.2–0.6 GB per episode)
path = snapshot_download(
    repo_id="skycn110/pico-robotics-basic",
    repo_type="dataset",
    allow_patterns="一楼体育馆自采/摆好普拉提圈/*",
)

import json, glob, numpy as np, cv2

case = sorted(glob.glob(f"{path}/一楼体育馆自采/摆好普拉提圈/case_*"))[0]

calib = json.load(open(f"{case}/undistort_camera.json"))
K = np.array(calib["left"]["K_new"])            # intrinsics for the undistorted video

pose = np.load(f"{case}/video_stream_head_pose_6dof.npz")
depth = np.load(f"{case}/video_stream_depth_frames.npz")
print("pose arrays:", pose.files, " depth arrays:", depth.files)

cap = cv2.VideoCapture(f"{case}/head_left_camera_undistorted.mp4")
ok, frame = cap.read()                          # 1456 × 1920 × 3

Intended uses

Egocentric video and depth pretraining for manipulation and embodied-AI models
Visual odometry, head-motion forecasting, and egocentric world models
Stereo and monocular depth estimation from a head-mounted viewpoint
Studying human shelf-work and tidying behaviour as a source for robot imitation

Limitations

Two environments only; task counts are uneven (several tasks have fewer than 10 episodes).
No hand pose or action labels in this edition — see the Advanced and Commercial editions.
218 earlier retail episodes lack depth, head pose and audio (see Earlier captures above).

License

CC BY-NC 4.0. Free for research and other non-commercial use with attribution. Training or evaluating models for commercial products requires a commercial license — contact jiuchen@openelephant.ai.

Citation

@misc{openelephant_pico_egocentric_2026,
  title        = {Pico Egocentric Dataset},
  author       = {{OpenElephant Intelligence}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/datasets/skycn110/pico-robotics-basic}}
}

Contact

Jiuchen, OpenElephant Intelligence · jiuchen@openelephant.ai

Commercial licensing, larger samples, custom collection and research collaborations are all welcome. Questions about the data can also go in this repository's Discussions tab.

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