sarcos

**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](https://www.datarobot.com/blog/running-code-and-failing-models/) 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/