This　paper　presents　a　self-learning　model　to　help　agents　learn　the　sensorimotor　skills.　The　model　includes　the　sensory　part,　the　motorial　part,　the　sensorimotor　map　and　the　learning　mechanism.　At　every　learning　step,　the　agent　senses　its　states　in　its　internal　environment,　executes　motions　based　on　the　sensorimotor　map,　and　at　the　same　time　gets　a　reward　from　the　external　environment　as　the　result　of　its　behavior.　Then　the　sensorimotor　map　is　tuned　according　to　the　learning　mechanism　which　is　designed　based　on　the　theory　of　Skinner　operant　conditioning.　The　convergence　of　learning　mechanism　is　proved.　To　show　the　model＇s　ability　of　self-learning,　the　paper　first　simulated　the　famous　Skinner　pigeon　experiment,　and　then　used　the　model　to　a　robot　with　the　task　of　right　handshake.　Both　of　the　results　show　that　the　model　designed　is　intelligent　and　can　help　agents　learn　the　sensorimotor　skills.　©　2015　IEEE.