---
license: apache-2.0
task_categories:
- robotics
tags:
- LeRobot
- robotics
- lerobot
- vla
configs:
- config_name: default
  data_files: data/*/*.parquet
---

This dataset was created using [LeRobot](https://github.com/huggingface/lerobot).

## Dataset Description



- **Homepage:** [More Information Needed]
- **Paper:** [More Information Needed]
- **License:** apache-2.0

## Dataset Structure

[meta/info.json](meta/info.json):
```json
{
    "codebase_version": "v3.0",
    "robot_type": "seeed_b601_rs_follower",
    "total_episodes": 100,
    "total_frames": 42746,
    "total_tasks": 1,
    "chunks_size": 1000,
    "data_files_size_in_mb": 100,
    "video_files_size_in_mb": 200,
    "fps": 30,
    "splits": {
        "train": "0:100"
    },
    "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",
    "features": {
        "action": {
            "dtype": "float32",
            "names": [
                "shoulder_pan.pos",
                "shoulder_lift.pos",
                "elbow_flex.pos",
                "wrist_flex.pos",
                "wrist_yaw.pos",
                "wrist_roll.pos",
                "gripper.pos"
            ],
            "shape": [
                7
            ]
        },
        "observation.state": {
            "dtype": "float32",
            "names": [
                "shoulder_pan.pos",
                "shoulder_lift.pos",
                "elbow_flex.pos",
                "wrist_flex.pos",
                "wrist_yaw.pos",
                "wrist_roll.pos",
                "gripper.pos"
            ],
            "shape": [
                7
            ]
        },
        "observation.images.wrist": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.wrist_depth": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.overhead": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "observation.images.overhead_depth": {
            "dtype": "video",
            "shape": [
                480,
                640,
                3
            ],
            "names": [
                "height",
                "width",
                "channels"
            ],
            "info": {
                "video.height": 480,
                "video.width": 640,
                "video.codec": "h264",
                "video.pix_fmt": "yuv420p",
                "video.is_depth_map": false,
                "video.fps": 30,
                "video.channels": 3,
                "has_audio": false
            }
        },
        "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
        }
    }
}
```


## Citation

**BibTeX:**

```bibtex
[More Information Needed]
```

## Depth (RGB-D)

This dataset includes raw metric depth alongside the RGB videos above, for
overhead, wrist. It is
**not** part of the standard LeRobot schema, so generic loaders will not see
it automatically.

```
depth/<camera>_depth/episode_NNNNNN.mkv   FFV1, gray16le, lossless uint16
meta/depth_info.json                      depth_scale_m + intrinsics per camera
```

Depth is aligned to its colour camera on capture, so `depth[y, x]` and the
matching `observation.images.<camera>[y, x]` are the same physical point.
`metres = pixel_value * depth_scale_m`; `0` means no return.

Read it with `read_depth.py` from the collection tooling used to record this
dataset, or directly:

```python
import av, json, numpy as np
info = json.load(open("meta/depth_info.json"))
with av.open("depth/wrist_depth/episode_000000.mkv") as c:
    depth = np.stack([f.to_ndarray() for f in c.decode(video=0)])
depth_m = depth.astype(np.float32) * info["cameras"]["wrist_depth"]["depth_scale_m"]
```