In　order　to　make　mobile　robot　more　bionic　and　intelligent,　a　goal-directed　behavioral　learning　model　based　on　hippocampal-striatal　circuit　(GBL-HSC)　is　proposed,　which　attempts　to　explore　the　physiological　mechanisms　of　goal-directed　navigation.　Hippocampal-striatal　neural　circuit　(HP-Str　circuit)　is　increasingly　being　considered　as　an　appropriate　model　system　to　understand　goal-directed　decision-making　and　planning　mechanisms　in　the　brain.　Ilippocampus　provides　a　place　cells-based　cognitive　map　of　the　environment,　and　makes　behavioral　decisions　according　to　a　sequence　planning　algorithm　of　place　cells.　Striatum　includes　the　striosome　and　the　matrix,　with　the　former　receives　dopamine　reward　signal　from　substantia　nigra　and　outputs　evaluation　information　of　the　behavior,　which　is　used　by　the　matrix　to　choose　actions.　The　agent　chooses　action　at　decision　point　based　on　confidence　in　the　prefrontal　cortex.　Simulation　experiments　of　navigation　in　Tolman　detour　maze　is　conducted.　Results　show　that　the　model　can　help　agent　to　learn　the　optimal　behavioral　decision,　to　quickly-　adapt　to　changes　in　the　environment,　such　as　choosing　a　detour　path,　thus　realizing　a　better　and　faster　navigation.