Comparison of Data-Driven Approaches to Configuration Space Approximation

Configuration spaces (C-spaces) are an essential component of many robot path-planning algorithms, yet calculating them is a time-consuming task, especially in spaces involving a large number of degrees of freedom (DoF). Here we explore a two-step data-driven approach to C-space approximation: (1) sample (i.e., explicitly calculate) a few configurations; (2) train a machine learning (ML) model on these configurations to predict the collision status of other points in the C-space. We studied multiple factors that impact this approximation process, including model representation, number of DoF (up to 42), collision density, sample size, training set distribution, and desired confidence of predictions. We conclude that XGBoost offers a significant time improvement over other methods, while maintaining low error rates, even in C-Spaces with over 14 DoF.