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
11.5
task_index
int64
0
0
episode_index
int64
0
3
frame_index
int64
0
115
index
int64
0
177
[ 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 ]
0
0
1
0
14
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.1
0
1
1
15
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.2
0
1
2
16
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.3
0
1
3
17
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.4
0
1
4
18
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.5
0
1
5
19
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.6
0
1
6
20
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.7
0
1
7
21
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.8
0
1
8
22
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
0.9
0
1
9
23
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1
0
1
10
24
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.1
0
1
11
25
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.2
0
1
12
26
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.3
0
1
13
27
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.4
0
1
14
28
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.5
0
1
15
29
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.6
0
1
16
30
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.7
0
1
17
31
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.8
0
1
18
32
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
1.9
0
1
19
33
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2
0
1
20
34
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.1
0
1
21
35
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.2
0
1
22
36
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.3
0
1
23
37
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.4
0
1
24
38
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.5
0
1
25
39
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.6
0
1
26
40
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.7
0
1
27
41
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.8
0
1
28
42
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
2.9
0
1
29
43
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3
0
1
30
44
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.1
0
1
31
45
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.2
0
1
32
46
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.3
0
1
33
47
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.4
0
1
34
48
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.5
0
1
35
49
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.6
0
1
36
50
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.7
0
1
37
51
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.8
0
1
38
52
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
3.9
0
1
39
53
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4
0
1
40
54
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.1
0
1
41
55
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.2
0
1
42
56
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.3
0
1
43
57
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.4
0
1
44
58
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.5
0
1
45
59
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.6
0
1
46
60
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.7
0
1
47
61
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.8
0
1
48
62
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
4.9
0
1
49
63
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5
0
1
50
64
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.1
0
1
51
65
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.2
0
1
52
66
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.3
0
1
53
67
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.4
0
1
54
68
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.5
0
1
55
69
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.6
0
1
56
70
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.7
0
1
57
71
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.8
0
1
58
72
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
5.9
0
1
59
73
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6
0
1
60
74
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.1
0
1
61
75
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.2
0
1
62
76
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.3
0
1
63
77
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.4
0
1
64
78
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.5
0
1
65
79
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.6
0
1
66
80
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.7
0
1
67
81
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.8
0
1
68
82
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
6.9
0
1
69
83
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7
0
1
70
84
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.1
0
1
71
85
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.2
0
1
72
86
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.3
0
1
73
87
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.4
0
1
74
88
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.5
0
1
75
89
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.6
0
1
76
90
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.7
0
1
77
91
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.8
0
1
78
92
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
7.9
0
1
79
93
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
8
0
1
80
94
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
8.1
0
1
81
95
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
8.2
0
1
82
96
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
8.3
0
1
83
97
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
8.4
0
1
84
98
[ 0, 0, 0, 0, 0, 0 ]
[ 0, 0, 0, 0, 0, 0 ]
8.5
0
1
85
99
End of preview. Expand in Data Studio

test_mq_6

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
34