sarcos
OpenML dataset with id 43873
Author name not available (Why is that?)
Full work available at URL: https://api.openml.org/data/v1/download/22102750/sarcos.arff
Upload date: 20 April 2022
Dataset Characteristics
Number of classes: 0
Number of features: 22 (numeric: 22, symbolic: 0 and in total binary: 0 )
Number of instances: 44,484
Number of instances with missing values: 0
Number of missing values: 0
Dataset Description
The data relates to an inverse dynamics problem for a seven degrees-of-freedom SARCOS anthropomorphic robot arm. 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. Usually, the first of those (V22) is used as the target variable and is therefore set as the default target variable, while the other 6 joint torques are excluded from the model.
NOTE
This dataset contains only the corresponding training data, as there is data leakage between the original training and test data. This was described in this article by Rajiv Shah.
Related Studies
* LWPR: An O(n) Algorithm for Incremental Real Time Learning in High Dimensional Space, S. Vijayakumar and S. Schaal, Proc ICML 2000, 1079-1086
* (2000). Statistical Learning for Humanoid Robots, S. Vijayakumar, A. D'Souza, T. Shibata, J. Conradt, S. Schaal, Autonomous Robot, 12(1) 55-69
* (2002) Incremental Online Learning in High Dimensions S. Vijayakumar, A. D'Souza, S. Schaal, Neural Computation 17(12) 2602-2634 (2005)
* (2019) Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning, Sahand Rezaei-Shoshtari, David Meger, Inna Sharf, https://arxiv.org/pdf/1910.02291.pdf
* (2019) TabNet: Attentive Interpretable Tabular Learning, Sercan Oe. Arik, Tomas Pfister, https://arxiv.org/pdf/1908.07442.pdf
Citation
LWPR: An O(n) Algorithm for Incremental Real-Time Learning in High Dimensional Space, S. Vijayakumar and S. Schaal, Proc ICML 2000, 1079-1086 (2000).
The data was obtained from: http://www.gaussianprocess.org/gpml/data/
This page was built for dataset: sarcos
