Apprenticeship Learning for Motion Planning with Application to Parking Lot Navigation

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. Authors: Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebastian Thrun (2008)
AUTHORED BY
Pieter Abbeel
Dmitri Dolgov
Andrew Y. Ng
Sebastian Thrun

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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