In　this　work,　a　behavior-based　adaptive　dynamic　programming　(BADP)　method　is　proposed　for　the　coordination　control　of　unmanned　ground　vehicle-manipulator　systems　(UGVMs).　Through　a　null-space-based　behavioral　control　(NSBC)　framework,　the　multi-objective　coordination　control　is　transformed　into　a　single-objective　tracking　control　at　the　mission　layer.　Since　cost　functions　and　control　constraints　are　simplified　at　the　control　layer,　the　complexity　of　controller　design　is　reduced.　Then,　an　identifier-actor-critic　reinforcement　learning　algorithm　framework　is　introduced　to　learn　the　optimal　control　policy　by　balancing　the　control　performance　and　consumption.　Simulation　results　show　that　control　costs　are　reduced　by　around　13.5%　per　sampling　period　compared　to　existing　multiple　objective　control　methods.　Finally,　the　BADP　method　is　experimentally　validated　using　four　real　UGVMs.