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List Prediction Applied To Motion Planning

Abhijeet Tallavajhula, Sanjiban Choudhury, Sebastian Scherer and Alonzo Kelly
Conference Paper, 2016 IEEE International Conference on Robotics and Automation (ICRA), May, 2016

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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.

BibTeX Reference
title = {List Prediction Applied To Motion Planning},
author = {Abhijeet Tallavajhula and Sanjiban Choudhury and Sebastian Scherer and Alonzo Kelly},
booktitle = {2016 IEEE International Conference on Robotics and Automation (ICRA)},
month = {May},
year = {2016},