Active Training Data Selection for Gaussian Process-based Robot Dynamics Learning and Control

Abstract

Model-based robot control requires an accurate dynamics model and a machine learning-based method can extract robot dynamics from collected motion data by simulation and experiment. A Gaussian process (GP) has been used as one of the learning methods to obtain robot dynamics. To avoid large training datasets for learning robot dynamics, we propose an active training data selection strategy. The data sampling criteria are to minimize the probability density difference between the actual model and the GP-based estimate. Using such a criterion, the active training data strategy identifies where to sample the next data point for model training. We demonstrate the proposed active learning strategy with a 3-link robot arm in both fully actuated and underactuated modes. With the selected dataset containing 150 data points, the integrated probability density error compared with the entire dataset (over 30, 000 data points) is less than 0.3. The experimental results confirm that the GP-based control performance is greater than that under the model-based control.