Human-like　motion　is　necessary　for　successful　human-robot　collaboration.　To　achieve　human-like　motion　with　generalization　capability　on　a　robotic　arm,　we　use　dynamic　movement　primitives　(DMPs)　to　model　the　motion　of　the　human　arm,　i.e.,　DMPs　extract　parameters　from　the　trajectories　involving　position　and　orientation　of　the　hand,　the　elbow　elevation　angle.　When　using　DMPs　for　human-like　motion　generation,　i.e.,　planning　human-like　motion　trajectories　for　robotic　arms,　the　DMPs　must　be　provided　with　targets.　The　task　performed　provides　targets　of　end-effector　position　and　orientation　for　the　DMPs.　We　use　a　deep　neural　network　(DNN)　to　provide　the　target　of　elbow　elevation　angle.　The　proposed　method　is　validated　in　a　simulation　environment,　and　the　Pearson　correlation　coefficient　is　used　to　evaluate　the　human-like　level　of　the　motion.　The　results　indicate　a　high　anthropomorphism　of　the　robotic　arm　in　performing　generalized　motions.　©　2023　Technical　Committee　on　Control　Theory,　Chinese　Association　of　Automation.