There is growing interest in applying machine learning to motion planning. Potential applications are pre- dicting an initial seed for trajectory optimization, predicting an effective heuristic for search based planning, and even predicting a planning algorithm for adaptive motion planning systems. In these situations, providing only a single prediction is unsatisfactory. It leads to many scenarios where the prediction suffers a high loss. In this paper, we advocate list prediction. Each predictor in a list focusses on different regions in the space of environments. This overcomes the shortcoming of a single predictor, and improves overall performance. A framework for list prediction, CONSEQOPT, already exists. Our contribution is an extensive domain-specific treatment. We provide a rigorous and clear exposition of the procedure for training a list of predictors. We provide experimental results on a spectrum of motion planning applications. Each application contributes to understanding the behavior of list prediction. We observe that the benefit of list prediction over a single prediction is significant, irrespective of the application.
|List Prediction Applied To Motion Planning
Abhijeet Tallavajhula, Sanjiban Choudhury, Sebastian Scherer, Alonzo Kelly
2016 IEEE International Conference on Robotics and Automation (ICRA) May, 2016
|List prediction for motion planning: Case studies
Abhijeet Tallavajhula and Sanjiban Choudhury
CMU-RI-TR-15-25 October, 2015