README.md

---
license: cc-by-4.0
task_categories:
  - robotics
  - video-classification
language:
  - en
tags:
  - pico
  - vr
  - tracker
  - action-recognition
  - segmentation
  - point-cloud
  - hand-tracking
  - mcap
  - egocentric
  - retail
  - robotics-learning
  - embodied-ai
pretty_name: Pico Robotics Dataset - Annotated
size_categories:
  - 10K<n<100K
extra_gated_prompt: >-
  Access to this dataset is reviewed manually. Please describe your intended use
  and affiliation. Requests are typically answered within TBD business days.
configs:
  - config_name: retail-shelf-a
    data_files: retail-shelf-a/**
  - config_name: retail-shelf-b
    data_files: retail-shelf-b/**
  - config_name: gymnasium
    data_files: gymnasium/**
---

# Pico Robotics Dataset · Annotated Edition

> **Egocentric multimodal capture from a Pico VR headset + custom tracker rig — Annotated tier**

Builds on the Advanced edition by adding coarse action segmentation. Every sequence is divided
into labelled temporal segments, so the data can be used directly for action recognition,
temporal segmentation, and behaviour-understanding tasks without an annotation pass of your own.

🔒 **This is a gated dataset.** Access requests are reviewed manually; submit one from the
dataset page.

---

## Editions

| Edition | Contents | Access |
|---|---|---|
| **Basic** | Undistorted stereo video + per-frame depth maps + dual-channel audio + ~1 kHz 6-DoF head pose + camera calibration | 🟢 [Public](https://huggingface.co/datasets/skycn110/pico-robotics-basic) |
| **Advanced** | Everything in Basic + pose-aligned stereo point clouds (`.npz`) + 21-joint hand tracking + world-frame point clouds + MCAP / Foxglove visualization | 🔒 [Request access](https://huggingface.co/datasets/skycn110/pico-robotics-advanced) |
| **Annotated** (this repo) | Everything in Advanced + coarse action segmentation (`segments.json`) | 🔒 Manual review |

---

## Scenes

| Config | Environment | Description | Sequences | Segments | Duration |
|---|---|---|---|---|---|
| `retail-shelf-a` | Convenience store, storefront area | Beverage gondolas, snack shelving, chest freezer, liquor display; strong daylight through a glass facade with mixed indoor lighting | TBD | TBD | TBD |
| `retail-shelf-b` | Convenience store, interior aisles | Branded beverage coolers, free-standing snack racks, bottled-water pallets, tiled floor under uniform ceiling lighting | TBD | TBD | TBD |
| `gymnasium` | Indoor sports arena | Large open court with line markings, tiered seating, overhead truss lighting, banners, and event-setup activity | TBD | TBD | TBD |

```python
from datasets import load_dataset

ds = load_dataset("skycn110/pico-robotics-annotated", "retail-shelf-a")
```

---

## Directory structure

```
retail-shelf-a/
└── sample_0001/
    ├── head_left_camera_undistorted.mp4
    ├── head_right_camera_undistorted.mp4
    ├── depth/
    ├── audio_dual_channel.wav
    ├── undistort_camera.json
    ├── video_index.json
    ├── pointcloud/
    ├── pointcloud_world/
    ├── hand_landmarks.json
    ├── recording.mcap
    └── segments.json                       # Coarse action segmentation  ← new in this tier
```

Everything except `segments.json` is documented in the
[Advanced README](https://huggingface.co/datasets/skycn110/pico-robotics-advanced).

---

## Annotation format

### `segments.json`

```json
{
  "sample_id": "sample_0001",
  "scene": "retail-shelf-a",
  "fps": 30,
  "segments": [
    {
      "id": 0,
      "start_frame": 0,
      "end_frame": 148,
      "start_time": 0.0,
      "end_time": 4.93,
      "label": "approach_shelf",
      "notes": ""
    }
  ]
}
```

| Field | Type | Description |
|---|---|---|
| `start_frame` / `end_frame` | int | Inclusive frame range, indexed against `video_index.json` |
| `start_time` / `end_time` | float | Seconds from sequence start |
| `label` | string | Action class, see the label set below |
| `notes` | string | Free-text annotator remark, may be empty |

Segments within a sequence are contiguous and non-overlapping; frames that fit no class are
labelled `other`.

### Label set

TBD — list the action classes here, with a one-line definition and the frame count for each,
for example:

| Label | Definition | Segments |
|---|---|---|
| `approach_shelf` | Operator walks toward a shelf until stationary in front of it | TBD |
| `scan_shelf` | Head sweeps across shelf contents without hand motion | TBD |
| `reach_and_grasp` | Hand extends toward a product and closes on it | TBD |
| `inspect_item` | Held item brought toward the camera and rotated | TBD |
| `place_back` | Held item returned to the shelf | TBD |
| `walk_transit` | Locomotion between areas | TBD |
| `other` | Anything not covered above | TBD |

---

## Annotation protocol

| | |
|---|---|
| Annotators | TBD |
| Guidelines | TBD (link to the written protocol) |
| Boundary tolerance | TBD frames |
| Double-annotated portion | TBD % |
| Inter-annotator agreement | TBD |

Labels are deliberately **coarse**: boundaries are approximate and the class vocabulary is
small. They are intended as weak supervision or as a starting point for finer annotation, not
as a precision benchmark.

---

## Quick start

```python
import json

with open("retail-shelf-a/sample_0001/segments.json") as f:
    seg = json.load(f)

for s in seg["segments"]:
    print(f"{s['start_time']:6.2f}–{s['end_time']:6.2f}s  {s['label']}")
```

Clip a segment with the video:

```python
import cv2

cap = cv2.VideoCapture("retail-shelf-a/sample_0001/head_left_camera_undistorted.mp4")
s = seg["segments"][0]
cap.set(cv2.CAP_PROP_POS_FRAMES, s["start_frame"])

for _ in range(s["end_frame"] - s["start_frame"] + 1):
    ok, frame = cap.read()
    if not ok:
        break
```

---

## Intended uses

- Egocentric action recognition and temporal action segmentation
- Weakly supervised pretraining for behaviour understanding
- Video–language grounding of first-person activity
- Segment-conditioned imitation learning

### Out of scope

Class balance is uneven and follows whatever occurred naturally during capture; some labels have
very few segments. Boundaries are coarse and were not adjudicated frame-by-frame. Reporting
state-of-the-art numbers on this label set without acknowledging those limits would be
misleading.

---

## License

Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). When using this
dataset, please attribute:

> *Pico Robotics Dataset by skycn110, licensed under CC BY 4.0*

### Citation

```bibtex
@misc{skycn110_pico_robotics_annotated,
  title  = {Pico Robotics Dataset: Annotated Edition},
  author = {skycn110},
  year   = {2026},
  url    = {https://huggingface.co/datasets/skycn110/pico-robotics-annotated}
}
```

---

## Contact

- Data questions / collaboration: skycn110@gmail.com
- Access requests: use the form on this dataset page (manually reviewed)
- Issues and Discussions: welcome on this repository