---
license: cc-by-nc-4.0
task_categories:
- robotics
tags:
- lerobot
- robot-learning
- retail
- egocentric
configs:
- config_name: SABER-stream1
  data_files: SABER-stream1/data/*/*.parquet
- config_name: SABER-stream2
  data_files: SABER-stream2/data/*/*.parquet
- config_name: SABER-stream3
  data_files: SABER-stream3/data/*/*.parquet
extra_gated_heading: "Access Request for DreamVu SABER-10K Dataset"
extra_gated_fields:
  Full Name: text
  Email (institutional or corporate preferred): text
  Organization / Company: text
  Brief Description of Intended Use: text
  I agree to the CC BY-NC 4 license terms: checkbox
  I will not redistribute this dataset: checkbox
  I will cite DreamVu in any publications using this data: checkbox
  I confirm this dataset will not be used to train commercial products without a separate license: checkbox
---

# SABER-10K

A real-world egocentric dataset of retail manipulation skills collected by [DreamVu](https://dreamvu.com), formatted for [LeRobot](https://github.com/huggingface/lerobot) (v2.0).

The dataset covers retail environment navigation and manipulation recorded from a first-person (ego-view) perspective across three data streams. All episodes have passed a human deface quality-control audit.

## Dataset Summary

| Stream | Embodiment | Episodes | Frames | Tasks | Action Dim | Video Resolution |
|--------|------------|----------|--------|-------|------------|-----------------|
| SABER-stream1 | Egocentric (latent) | 5,000 | 1,438,845 | 4,260 | 4 | 640×480 |
| SABER-stream2 | Humanoid (72-DOF) | 200 | 38,204 | 200 | 72 | 640×360 |
| SABER-stream3 | Dexterous hands (36-DOF) | 4,800 | 1,377,496 | 4,800 | 36 | 640×360 |
| **Total** | | **10,000** | **2,854,545** | | | |

## Streams

### SABER-stream1

Egocentric retail navigation with actions encoded using [LAPA](https://github.com/NarsimhaMenga/LAPA) (Latent Action Pretraining from Videos), a codebook-based action quantization model trained on large-scale egocentric video. Each frame has 4 discrete latent action codes (codebook size 8).

- **FPS:** ~30 (29.97)
- **Video:** `observation.images.ego_view` — 640×480, H.264
- **Action:** `float32[4]` — LAPA latent codes (`latent_0` … `latent_3`)
- **State:** `float32[1]` — dummy placeholder
- **Tasks:** 4,260 unique natural-language task descriptions

### SABER-stream2

Full-body humanoid retargeted data in retail environments. State and action are 72-DOF joint configurations covering the entire body (legs, waist, arms, wrists, fingers, root pose and EEF poses) of a unitree g1 robot. 

- **FPS:** ~30 (29.97)
- **Video:** `observation.images.ego_view` — 640×360, MP4V
- **Action:** `float32[72]` — full-body joint targets (root pose + 65 joints)
- **State:** `float32[72]` — full-body joint positions
- **Tasks:** 200 unique natural-language task descriptions

### SABER-stream3

Dexterous hand manipulation in retail environments. State and action cover both hand poses and finger joint angles (36-DOF) of an inspire 5 finger gripper.

- **FPS:** ~30 (29.97)
- **Video:** `observation.images.ego_view` — 640×360, MP4V
- **Action:** `float32[36]` — hand pose + finger joints (left + right)
- **State:** `float32[36]` — hand pose + finger joint positions
- **Tasks:** 4,800 unique natural-language task descriptions

## Parquet Schema

### SABER-stream1

| Column | Type | Description |
|--------|------|-------------|
| `index` | int64 | Global frame index across all episodes |
| `episode_index` | int64 | Episode index within the stream |
| `timestamp` | float32 | Time in seconds from episode start |
| `task_index` | int64 | Index into `meta/tasks.jsonl` |
| `observation.state` | float32[1] | Dummy state placeholder |
| `action` | float32[4] | LAPA latent action codes |
| `next.reward` | float32 | Reward signal |
| `next.done` | bool | Episode termination flag |
| `annotation.human.action.instruction` | int64 | Task index (mirrors `task_index`) |
| `annotation.human.validity` | int64 | Human validity annotation |

### SABER-stream2 / SABER-stream3

| Column | Type | Description |
|--------|------|-------------|
| `index` | int64 | Global frame index across all episodes |
| `episode_index` | int64 | Episode index within the stream |
| `frame_index` | int64 | Frame index within the episode |
| `timestamp` | float32 | Time in seconds from episode start |
| `task_index` | int64 | Index into `meta/tasks.jsonl` |
| `observation.state` | float32[72 or 36] | Joint state vector |
| `action` | float32[72 or 36] | Joint action vector |

## Dataset Structure

Each stream follows the LeRobot v2.0 layout:

```
SABER-stream{N}/
├── meta/
│   ├── info.json               # Dataset metadata (fps, features, totals)
│   ├── episodes.jsonl          # Per-episode metadata (length, task indices)
│   ├── tasks.jsonl             # Task index → natural-language description
│   ├── stats.json              # Dataset-wide feature statistics
│   ├── modality.json           # Modality configuration
│   └── episode_mapping.csv     # New episode index → source episode index
├── data/
│   └── chunk-{NNN}/
│       ├── episode_000000.parquet
│       └── ...
└── videos/
    └── chunk-{NNN}/
        └── observation.images.ego_view/
            ├── episode_000000.mp4
            └── ...
```

## Usage

### With LeRobot

```bash
pip install lerobot
```

```python
from lerobot.common.datasets.lerobot_dataset import LeRobotDataset

dataset = LeRobotDataset(
    repo_id="DreamVu/SABER-10K",
    root="SABER-stream1",
)

print(f"Episodes: {dataset.num_episodes}")
print(f"Frames:   {len(dataset)}")

frame = dataset[0]
# dict_keys(['observation.images.ego_view', 'action', 'timestamp', ...])
```

### With HuggingFace `datasets`

```python
from datasets import load_dataset

ds = load_dataset("DreamVu/SABER-10K", name="SABER-stream1", split="train")
print(ds)
```

### Download a single stream

```bash
huggingface-cli download DreamVu/SABER-10K \
    --repo-type dataset \
    --include "SABER-stream1/**" \
    --local-dir ./SABER-10K
```

## License

This dataset is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). It is intended for non-commercial research use only.