Data
Grasping-Dataset

Grasping-Dataset

active ARFF GPL 2 Visibility: public Uploaded 24-03-2022 by Dustin Carrion
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Context At Shadow Robot, we are leaders in robotic grasping and manipulation. As part of our Smart Grasping System development, we're developing different algorithms using machine learning. This first public dataset was created to investigate the use of machine learning to predict the stability of a grasp. Due to the limitations of the current simulation, it is a restricted dataset - only grasping a ball. The dataset is annotated with an objective grasp quality and contains the different data gathered from the joints (position, velocity, effort). You can find all the explanations for this dataset over on Medium. Inspiration I'll be more than happy to discuss this dataset as well as which dataset you'd like to have to try your hands at solving real world robotic problems focused on grasping using machine learning. Let's connect on twitter (ugocupcic)!

29 features

experiment_number (ignore)string53937 unique values
0 missing
_robustnessnumeric45707 unique values
0 missing
_H1_F1J2_pos_numeric992492 unique values
0 missing
_H1_F1J2_vel_numeric992503 unique values
0 missing
_H1_F1J2_eff_numeric659481 unique values
0 missing
_H1_F1J3_pos_numeric992483 unique values
0 missing
_H1_F1J3_vel_numeric992504 unique values
0 missing
_H1_F1J3_eff_numeric659091 unique values
0 missing
_H1_F1J1_pos_numeric992496 unique values
0 missing
_H1_F1J1_vel_numeric992504 unique values
0 missing
_H1_F1J1_eff_numeric992475 unique values
0 missing
_H1_F3J1_pos_numeric992494 unique values
0 missing
_H1_F3J1_vel_numeric992504 unique values
0 missing
_H1_F3J1_eff_numeric992487 unique values
0 missing
_H1_F3J2_pos_numeric992494 unique values
0 missing
_H1_F3J2_vel_numeric992504 unique values
0 missing
_H1_F3J2_eff_numeric660519 unique values
0 missing
_H1_F3J3_pos_numeric992485 unique values
0 missing
_H1_F3J3_vel_numeric992502 unique values
0 missing
_H1_F3J3_eff_numeric657703 unique values
0 missing
_H1_F2J1_pos_numeric992497 unique values
0 missing
_H1_F2J1_vel_numeric992504 unique values
0 missing
_H1_F2J1_eff_numeric992495 unique values
0 missing
_H1_F2J3_pos_numeric992490 unique values
0 missing
_H1_F2J3_vel_numeric992503 unique values
0 missing
_H1_F2J3_eff_numeric697564 unique values
0 missing
_H1_F2J2_pos_numeric992487 unique values
0 missing
_H1_F2J2_vel_numeric992504 unique values
0 missing
_H1_F2J2_eff_numeric707393 unique values
0 missing
_measurement_numbernumeric30 unique values
0 missing

19 properties

992641
Number of instances (rows) of the dataset.
29
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
29
Number of numeric attributes.
0
Number of nominal attributes.
0
Number of attributes divided by the number of instances.
100
Percentage of numeric attributes.
Percentage of instances belonging to the most frequent class.
0
Percentage of nominal attributes.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
Average class difference between consecutive instances.
0
Percentage of missing values.

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