Dataset Viewer
robot_type
string | codebase_version
string | total_episodes
int64 | total_frames
int64 | total_tasks
int64 | total_videos
int64 | total_chunks
int64 | chunks_size
int64 | fps
int64 | splits
dict | data_path
string | video_path
string | features
dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
so-100
|
v2.1
| 0
| 0
| 1
| 0
| 1
| 1,000
| 30
|
{
"train": "0:0"
}
|
data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet
|
videos/chunk-{episode_chunk:03d}/{video_key}/episode_{episode_index:06d}.mp4
|
{
"action": {
"dtype": "float32",
"shape": [
6
],
"names": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6"
]
},
"observation.state": {
"dtype": "float32",
"shape": [
6
],
"names": [
"motor_1",
"motor_2",
"motor_3",
"motor_4",
"motor_5",
"motor_6"
]
},
"timestamp": {
"dtype": "float32",
"shape": [
1
],
"names": null
},
"episode_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"frame_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"task_index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"index": {
"dtype": "int64",
"shape": [
1
],
"names": null
},
"observation.images.main": {
"dtype": "video",
"shape": [
240,
320,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.fps": 30,
"video.codec": "avc1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
},
"observation.images.secondary_0": {
"dtype": "video",
"shape": [
240,
320,
3
],
"names": [
"height",
"width",
"channel"
],
"info": {
"video.fps": 30,
"video.codec": "avc1",
"video.pix_fmt": "yuv420p",
"video.is_depth_map": false,
"has_audio": false
}
}
}
|
BallReturn3
This dataset was generated using phosphobot.
This dataset contains a series of episodes recorded with a robot and multiple cameras. It can be directly used to train a policy using imitation learning. It's compatible with LeRobot.
To get started in robotics, get your own phospho starter pack..
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