This　paper　presents　an　OCPA　(operant　conditioning　probabilistic　automaton)　bionic　autonomous　learning　system　based　on　Skinner＇s　operant　conditioning　theory　for　solving　the　balance　control　problem　of　a　two-wheeled　flexible　robot.　The　OCPA　learning　system　consists　of　two　stages:　in　the　first　stage,　an　operant　action　is　selected　stochastically　from　a　set　of　operant　actions　and　then　used　as　the　input　of　the　control　system;　in　the　second　stage,　the　learning　system　gathers　the　orientation　information　of　the　system　and　uses　it　for　optimization　until　achieves　control　target.　At　the　same　time,　the　size　of　the　operant　action　set　can　be　automatically　reduced　during　the　learning　process　for　avoiding　little　probability　event.　Theory　analysis　is　made　for　the　designed　OCPA　learning　system　in　the　paper,　which　theoretically　proves　the　convergence　of　operant　conditioning　learning　mechanism　in　OCPA　learning　system,　namely　the　operant　action　entropy　will　converge　to　minimum　with　the　learning　process.　And　then　OCPA　learning　system　is　applied　to　posture　balanced　control　of　two-wheeled　flexible　self-balanced　robots.　Robot　does　not　have　posutre　balanced　skill　in　initial　state　and　the　selecting　probability　of　each　operant　in　operant　sets　is　equal.　With　the　learning　proceeding,　the　selected　probabilities　of　optimal　operant　gradually　tend　to　one　and　the　operant　action　entropy　gradually　tends　to　minimum,　and　so　robot　gradually　learned　the　posture　balanced　skill.　©　2011　South　China　University　of　Technology,　Academy　of　Mathematics　and　Systems　Science,　Chinese　Academy　of　Sciences　and　Springer-Verlag　Berlin　Heidelberg.