In　this　work,　a　behavior-based　adaptive　dynamic　programming　(BADP)　method　is　proposed　for　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　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　around　13.5%　per　sampling　period　compared　to　existing　multiple　objective　control　methods.　Finally,　the　BADP　method　is　experimentally　validated　using　four　real　UGVMs.