Dataset Viewer
Auto-converted to Parquet
Search is not available for this dataset
action
sequencelengths
6
6
observation.state
sequencelengths
6
6
timestamp
float64
0
5.4
task_index
int64
0
0
episode_index
int64
0
2
frame_index
int64
0
54
index
int64
0
78
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0
0
0
0
0
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.1
0
0
1
1
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.2
0
0
2
2
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.3
0
0
3
3
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.4
0
0
4
4
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.5
0
0
5
5
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.6
0
0
6
6
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.7
0
0
7
7
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.8
0
0
8
8
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.9
0
0
9
9
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1
0
0
10
10
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.1
0
0
11
11
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.2
0
0
12
12
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.3
0
0
13
13
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.4
0
0
14
14
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.5
0
0
15
15
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.6
0
0
16
16
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.7
0
0
17
17
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.8
0
0
18
18
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.9
0
0
19
19
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2
0
0
20
20
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.1
0
0
21
21
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.2
0
0
22
22
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.3
0
0
23
23
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.4
0
0
24
24
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.5
0
0
25
25
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.6
0
0
26
26
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.7
0
0
27
27
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.8
0
0
28
28
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.9
0
0
29
29
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3
0
0
30
30
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.1
0
0
31
31
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.2
0
0
32
32
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.3
0
0
33
33
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.4
0
0
34
34
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.5
0
0
35
35
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.6
0
0
36
36
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.7
0
0
37
37
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.8
0
0
38
38
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.9
0
0
39
39
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4
0
0
40
40
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.1
0
0
41
41
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.2
0
0
42
42
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.3
0
0
43
43
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.4
0
0
44
44
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.5
0
0
45
45
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.6
0
0
46
46
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.7
0
0
47
47
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.8
0
0
48
48
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.9
0
0
49
49
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5
0
0
50
50
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.1
0
0
51
51
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.2
0
0
52
52
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.3
0
0
53
53
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.4
0
0
54
54
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0
0
2
0
34
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.1
0
2
1
35
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.2
0
2
2
36
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.3
0
2
3
37
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.4
0
2
4
38
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.5
0
2
5
39
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.6
0
2
6
40
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.7
0
2
7
41
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.8
0
2
8
42
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.9
0
2
9
43
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1
0
2
10
44
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.1
0
2
11
45
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.2
0
2
12
46
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.3
0
2
13
47
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.4
0
2
14
48
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.5
0
2
15
49
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.6
0
2
16
50
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.7
0
2
17
51
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.8
0
2
18
52
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.9
0
2
19
53
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2
0
2
20
54
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.1
0
2
21
55
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.2
0
2
22
56
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.3
0
2
23
57
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.4
0
2
24
58
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.5
0
2
25
59
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.6
0
2
26
60
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.7
0
2
27
61
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.8
0
2
28
62
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.9
0
2
29
63
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3
0
2
30
64
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.1
0
2
31
65
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.2
0
2
32
66
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.3
0
2
33
67
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.4
0
2
34
68
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.5
0
2
35
69
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.6
0
2
36
70
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.7
0
2
37
71
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.8
0
2
38
72
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.9
0
2
39
73
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4
0
2
40
74
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.1
0
2
41
75
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.2
0
2
42
76
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.3
0
2
43
77
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.4
0
2
44
78
End of preview. Expand in Data Studio

image_mean

This dataset was generated using a phospho dev kit.

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 and RLDS.

Downloads last month
18