An　operant　conditioning　probabilistic　automation　(OCPA)　bionic　autonomous　learning　system　is　constructed　according　to　nonlinear,　strong-coupling　and　complex　two-wheeled　self-balancing　robot　dynamic　system.　The　OCPA　bionic　autonomous　learning　system　is　a　probabilistic　automaton　based　on　Skinner　operant　conditioning　whose　main　character　lies　in　simulating　the　operant　conditioning　mechanism　of　biology.　And　it　has　bionic　self-organization　function　which　contains　the　self-learning　and　adaptive　functions,　and　thus　the　OCPA　automaton　can　be　used　to　describe,　simulate　and　design　various　self-organization　systems.　The　convergence　of　operant　conditioning　learning　algorithm　of　OCPA　learning　system　is　proved　theoretically.　The　results　of　both　simulation　and　experiment　applied　to　two-wheeled　robot　poster　balance　control　indicate　that　the　OCPA　learning　system　does　not　require　the　robot　model,　and　the　motion　balanced　skills　of　robot　are　formed,　developed　and　perfected　gradually　by　simulating　the　operant　conditioning　mechanism　of　biology.