Dexterous　grasping　is　one　of　the　most　fundamental　abilities　of　robots　to　implement　various　manipulation　tasks.　Robots　should　have　the　same　ability　as　humans　to　plan　various　grasp　types　for　dexterous　grasping.　This　paper　addresses　the　problem　of　the　adaptability　of　grasp　planning.　A　novel　adaptive　grasp　planning　framework　is　designed　to　adapt　to　various　grasp　types　rather　than　a　single　one.　In　this　framework,　six　commonly　used　grasp　types　are　considered.　The　information　of　grasp　type　is　extracted　from　visual　data.　Then,　inspired　by　the　opposition　concept,　a　novel　concept　of　pregrasping　opposition　is　introduced　as　the　pregrasping　configuration　to　encode　the　information　of　the　grasp　type.　After　that,　a　two-stage　adaptive　grasp　planning　method　is　proposed,　which　determines　the　pregrasping　opposition　in　Stage　One　and　finds　a　feasible　grasp　configuration　for　object　grasping　in　Stage　Two.　The　pregrasping　opposition　is　used　as　a　waypoint　for　the　formation　of　complex　grasps.　The　effectiveness　of　the　proposed　framework　was　evaluated　in　simulation　and　real-world　experiments.　The　experimental　results　demonstrated　that　the　proposed　framework　can　plan　various　grasp　types　for　dexterous　robotic　grasping.　Additionally,　the　use　of　grasp　types　helps　to　reduce　the　complexity　of　grasp　planning　and　improve　the　grasp　dexterity　of　robotic　hands.　©　2021　Elsevier　B.V.