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Datasets:
prateeks-iu
/
pick_sugar_fixed

Tasks:
Robotics
Modalities:
Tabular
Time-series
Video
Formats:
parquet
Size:
10K - 100K
Tags:
LeRobot
robotics
lerobot
vla
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
 Community
1
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observation.state
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timestamp
float32
0
20.1
frame_index
int64
0
603
episode_index
int64
0
99
index
int64
0
42.7k
task_index
int64
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2.3
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2.333333
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2.4
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2.433333
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2.5
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2.533333
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2.566667
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2.6
78
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2.7
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2.8
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2.9
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2.933333
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2.966667
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3
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3.033333
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3.066667
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3.1
93
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3.133333
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3.166667
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3.2
96
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3.233333
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3.266667
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3.3
99
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99
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End of preview. Expand in Data Studio

This dataset was created using LeRobot.

Dataset Structure

meta/info.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": {
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    "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": {
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                "gripper.pos"
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                "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:

[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:

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"]
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