Cleaning of livestock buildings is the single most health-threatening task in the agricultural industry and a transition to robot-based cleaning would be instrumental to improving working conditions for employees. Present cleaning robots fall short on cleanness quality, as they cannot perform condition based cleaning. This paper describes how a novel sensor, developed for the purpose, and algorithms for classification and learning are combined with a commercial robot to obtain an autonomous system which meets the necessary quality attributes. These include features to make selective cleaning where dirty areas are detected, that operator assistance is called only when cleanness hypothesis cannot be made with confidence. The paper describes the design of the system where learning from experience maps and operator instructions are combined to obtain a smart and autonomous cleaning robot.