Environmental change adaptation for mobile robot navigation

Most of existing robot learning methods have considered the environment where their robots work unchanged, therefore, the robots have to learn from scratch if they encounter new environments. This paper proposes a method which adapts robots to environmental changes by efficiently transferring a learned policy in the previous environments into a new one and effectively modifying it to cope with these changes. The resultant policy (a part of state transition map) does not seem optimal in each individual environment, but may absorb the differences between multiple environments. We apply the method to a mobile robot navigation problem of which task is to reach the target avoiding obstacles based on uninterpreted sonar and visual information. Experimental results show the validity of the method and discussion is given.