Recently,　artificial　neural　networks　(ANNs)　have　been　applied　to　various　robot-related　research　areas　due　to　their　powerful　spatial　feature　abstraction　and　temporal　information　prediction　abilities.　Decision-making　has　also　played　a　fundamental　role　in　the　research　area　of　robotics.　How　to　improve　ANNs　with　the　characteristics　of　decision-making　is　a　challenging　research　issue.　ANNs　are　connectionist　models,　which　means　they　are　naturally　weak　in　long-term　planning,　logical　reasoning,　and　multistep　decision-making.　Considering　that　a　small　refinement　of　the　inner　network　structures　of　ANNs　will　usually　lead　to　exponentially　growing　data　costs,　an　additional　planning　module　seems　necessary　for　the　further　improvement　of　ANNs,　especially　for　small　data　learning.　In　this　paper,　we　propose　a　state　operator　and　result　(SOAR)　improved　ANN　(SANN)　model,　which　takes　advantage　of　both　the　long-term　cognitive　planning　ability　of　SOAR　and　the　powerful　feature　detection　ability　of　ANNs.　It　mimics　the　cognitive　mechanism　of　the　human　brain　to　improve　the　traditional　ANN　with　an　additional　logical　planning　module.　In　addition,　a　data　fusion　module　is　constructed　to　combine　the　probability　vector　obtained　by　SOAR　planning　and　the　original　data　feature　array.　A　data　fusion　module　is　constructed　to　convert　the　information　from　the　logical　sequences　in　SOAR　to　the　probabilistic　vector　in　ANNs.　The　proposed　architecture　is　validated　in　two　types　of　robot　multistep　decision-making　experiments　for　a　grasping　task:　a　multiblock　simulated　experiment　and　a　multicup　experiment　in　a　real　scenario.　The　experimental　results　show　the　efficiency　and　high　accuracy　of　our　proposed　architecture.　The　integration　of　SOAR　and　ANN　is　a　good　compromise　between　logical　planning　with　small　data　and　probabilistic　classification　with　big　data.　It　also　has　strong　potential　for　more　complicated　tasks　that　require　robust　classification,　long-term　planning,　and　fast　learning.　Some　potential　applications　include　recognition　of　grasping　order　in　multiobject　environment　and　cooperative　grasping　of　multiagents.