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.

• Type: Conference
• Year: 2008
• Where: IROS
• Authors: Pieter Abbeel, Dmitri Dolgov, Andrew Y. Ng, Sebastian Thrun
• Keywords: Complex Cost Functions, Cost Function, IROS 2008, Local Smoothing, Robotics