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Shaping and policy search for nearest-neighbour control policies with applications to vehicle steering

University of British Columbia

The graceful animal motion we see in nature has proven extremely difficult to reproduce algorithmically. There is a need for further research into motion control techniques to address this problem adequately for computer animation and robotics applications. In this thesis, we describe a novel method for the synthesis of compact control policies for a variety of motion control problems. Direct policy search is applied to a nearest-neighbour control policy, which uses a Voronoi cell discretization of the observable state space, as induced by a set of control nodes located in this space. Such a semi-parametric representation allows for policy refinement through the adaptive addition of nodes. Thus, a coarse-to-fine policy search can be performed in such a way that the problem is shaped for easy learning. We apply our method to developing policies for steering various vehicles around a winding track. In particular, a policy is learned to steer a double-trailer truck backwards, a problem of considerable difficulty given the instability of the trailers. We also generate policies for the problems of balancing an inverted pendulum on a moving cart and driving the state of a point mass around a user-specified limit cycle. We examine these motion control results critically and discuss the limitations of the nearest-neighbour policy representation and our policy search method.

Thesis/Dissertation

application/pdf

2009-12-02

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http://hdl.handle.net/2429/16091

Computer Science

University of British Columbia

UBCV

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