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2008
IEEE

Apprenticeship learning for motion planning with application to parking lot navigation

10 years 3 months ago
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.
Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebast
Added 31 May 2010
Updated 31 May 2010
Type Conference
Year 2008
Where IROS
Authors Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebastian Thrun
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