Data
sarcos

sarcos

active ARFF Publicly available Visibility: public Uploaded 22-12-2022 by Sebastian Fischer
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  • Machine Learning Sociology study_353
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Data Description Within robotics, inverse dynamics algorithms are used to calculate the torques that a robot's motors must deliver to make the robot's end-point move in the way prescribed by its current task. More about [inverse dynamics][1]. [1]: The data set consists of 48933 data points, collected at 100Hz from the actual robot performing various rhythmic and discrete movement tasks (this corresponds to 7.5 minutes of data collection). Note that this version of the dataset contains both the train and test data from the original dataset, which includes many duplicates. The task is to map from a 21-dimensional input space (7 joint positions, 7 joint velocities, 7 joint accelerations) to the corresponding 7 joint torques. This version of the dataset includes only the training set of the original dataset, as the test dataset contains almost exclusively duplicates from the training data. Attribute Description 1. *V[1-7]* - 7 joint positions 2. *V[8-14]* - 7 joint velocities 3. *V[15-21]* - 7 joint accelerations 4. *V[22-28]* - 7 joint torques, target variables, take one (*V22*) as target feature, ignore others as alternate target features

22 features

V22 (target)numeric11414 unique values
0 missing
V1numeric34291 unique values
0 missing
V2numeric24862 unique values
0 missing
V3numeric25135 unique values
0 missing
V4numeric32846 unique values
0 missing
V5numeric24022 unique values
0 missing
V6numeric2871 unique values
0 missing
V7numeric2846 unique values
0 missing
V8numeric43877 unique values
0 missing
V9numeric42533 unique values
0 missing
V10numeric43765 unique values
0 missing
V11numeric44237 unique values
0 missing
V12numeric43172 unique values
0 missing
V13numeric42330 unique values
0 missing
V14numeric44109 unique values
0 missing
V15numeric44440 unique values
0 missing
V16numeric44364 unique values
0 missing
V17numeric44427 unique values
0 missing
V18numeric44457 unique values
0 missing
V19numeric44394 unique values
0 missing
V20numeric44332 unique values
0 missing
V21numeric44460 unique values
0 missing
V23 (ignore)numeric9635 unique values
0 missing
V24 (ignore)numeric8738 unique values
0 missing
V25 (ignore)numeric7907 unique values
0 missing
V26 (ignore)numeric5985 unique values
0 missing
V27 (ignore)numeric5998 unique values
0 missing
V28 (ignore)numeric5893 unique values
0 missing

19 properties

48933
Number of instances (rows) of the dataset.
22
Number of attributes (columns) of the dataset.
0
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.
22
Number of numeric attributes.
0
Number 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.
-3.09
Average class difference between consecutive instances.
0
Percentage of missing values.
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.

1 tasks

0 runs - estimation_procedure: 10-fold Crossvalidation - target_feature: V22
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