Vector quantization for state-action map compression
Ryuichi Ueda, Takeshi Fukase, Yuichi Kobayashi, Tamio Arai · 2004
It sounds clever to achieve intelligence of a mobile robot by means of pre-computed algorithm, because it can cut down computation on a small computer installed on the robot. However, the amount of pre-computed results is usually too large to store. This paper proposes a compression method for pre-computed data of dynamic programming. A vector quantization method is proposed with the studies on entropy evaluation. Robot motions in RoboCup are planned by means of dynamic programming. States on the optimal state-action map are once bounded into a neighboring group and then compressed into a tiny number of state-action. The distortion, the bad side effect of compression, is evaluated and minimized. The proposed method is verified on both simulations and experiments of robots.