Apprenticeship learning for motion planning, with application to parking lot navigation
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-08). September 2008.
Abstract
Motion and path-planning algorithms often use complex cost functions for both global navigation and local smoothing of trajectories. Obtaining good results typically requires carefully hand-engineering the trade-offs between different terms in the cost function. In practice, it is often much easier to demonstrate a few good trajectories. In this paper, we describe an efficient algorithm which---when given access to a few trajectory demonstrations---can automatically infer good trade-offs between the different costs. In our experiments, we apply our algorithm to the problem of navigating a robotic car in a parking lot.
BibTex
@inproceedings{ abbeel08invrlIROS,
paperID = "IROS-08",
month = "September",
author = "Pieter Abbeel and Dmitri Dolgov and Andrew Ng and Sebastian Thrun",
booktitle = "Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS-08)",
address = "Nice, France",
title = "Apprenticeship learning for motion planning, with application to parking lot navigation",
publisher = "IEEE",
year = "2008"
}
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