Inspired　by　Skinner＇s　operant　conditioning　theory,　an　operant　conditioning　learning　model　is　presented　to　deal　with　the　autonomous　learning　problem　in　cognitive　robotics.　The　model　is　described　by　nine　elements,　including　the　space　set,　the　action　set,　the　bionic　learning　function　and　the　system　entropy　etc.　A　notion　＇negative　ideal　rate＇　is　defined　to　compute　the　orientation　function.　The　OCLM　is　applied　to　solve　obstacle　avoidance　and　navigation　problems　for　mobile　robots.　The　experiment　results　show　that　the　robot　based　on　the　model　can　autonomously　learn　how　to　arrive　at　the　goal　in　a　collision-free　way　through　interaction　with　the　environment,　and　show　the　effectiveness　of　the　proposed　model.