Value iteration under the constraint of vector quantization for improving compressed state-action maps

Vector quantization (VQ) is a useful method for compressing data in an array such as images and sounds. We have applied it to motion planning for robots. Dynamic programming is used for planning and outputs the result as a large multi-dimensional array. The array is compressed with VQ, and installed on a robot whose memory space is smaller than the array. Some experiments have shown the potential of this method. In this paper, we improve this method with value iteration algorithms that are applied to a compressed map. Simulations verified the improvement of the compression ratio with the value iteration algorithms.