Abstract
In this paper, we present a real-time decision making method for a quadruped robot whose sensor and locomotion have large errors, considering the observational cost and the optimality. We make a State-Action Map by off-line planning considering the uncertainty of the robot's location with Dynamic Programming. Using this map, the robot can immediately decide optimal action which minimizes the time to reach a target state at any states. The number of observation, swinging its head, is also minimized by taking the time cost of observation into account. We compress this map for implementation with Vector Quantization. The total loss of optimality through compression is minimized by using the differences of the values between the optimal action and the others. In the simulation, the performance of some soccer behaviors were improved in comparison with current methods. The low computation under the restriction of the memory was verified in the experiment.
