2007 RSS RSS 2007

Predicting Partial Paths from Planning Problem Parameters

Abstract

Many robot motion planning problems can be described as a combination of motion through relatively sparsely filled regions of configuration space and motion through tighter passages. Sample-based planners perform very effectively everywhere but in the tight passages. In this paper, we provide a method for parametrically describing workspace arrangements that are difficult for planners, and then learning a function that proposes partial paths through them as a function of the parameters. These suggested partial paths are then used to significantly speed up planning for new problems. Download: Bibtex: @INPROCEEDINGS{ Finney-RSS-07, AUTHOR = {S. Finney and L. Kaelbling and T. Lozano-Perez}, TITLE = {Predicting Partial Paths from Planning Problem Parameters}, BOOKTITLE = {Proceedings of Robotics: Science and Systems}, YEAR = {2007}, ADDRESS = {Atlanta, GA, USA}, MONTH = {June}, DOI = {10.15607/RSS.2007.III.006} }

🌉 Interdisciplinary Bridge — Machine Learning and Robotics
🧭 Keyword Pioneer — partial path prediction
🐣 Hot Topic Early Bird — function approximation
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics