Apprenticeship learning for motion planning with application to parking lot navigation
Abbeel, P.
Dolgov, D.
Ng, A.Y.
Thrun, S.
Comput. Sci. Dept., Stanford Univ., Stanford, CA;
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.
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