__Abstract__
We present a semi-parametric control policy
representation and use it to solve a series of nonholonomic
control problems with input state spaces of up to 7 dimensions.
A nearest-neighbor control policy is represented by a set of
nodes that induce a Voronoi partitioning of the input space.
The Voronoi cells then define local control actions. Direct
policy search is applied to optimize the node locations and
actions. The selective addition of nodes allows for progressive
refinement of the control representation. We demonstrate this
approach on the challenging problem of learning to steer
cars and trucks-with-trailers around winding tracks with
sharp corners. We consider the steering of both forwards and
backwards-moving vehicles with only local sensory information. The steering behaviors for these nonholonomic systems
are shown to generalize well to tracks not seen in training
__Paper__
[PDF (0.3 Mb)](https://www.cs.ubc.ca/~van/papers/2005-icra-steering.pdf)
__Video__
__bibtex__
`````````````````````````
@inproceedings{alton2005learning,
title={Learning to steer on winding tracks using semi-parametric control policies},
author={Alton, Ken and Van De Panne, Michiel},
booktitle={Proceedings of the 2005 IEEE International Conference on Robotics and Automation},
pages={4588--4593},
year={2005},
organization={IEEE}
}
`````````````````````````