This　paper　proposes　a　specific　and　effective　real-time　sequence　planning　method　using　robot　manipulators　to　complete　complex　assembly　tasks.　Many　previous　studies　developed　different　traversal　methods　to　obtain　the　optimal　assembly　sequence.　Besides,　a　number　of　algorithms　were　proposed　to　enhance　flexibility　when　the　conditions　or　rules　were　changed　in　various　sequence　optimization　problems.　However,　these　state-of-the-art　(STOA)　methods　necessarily　require　modifications　when　task　details　are　changed.　Consequently,　to　further　improve　the　generalization　ability　and　improve　the　performance　of　the　sequence　optimization,　a　neural　combinatorial　optimization　algorithm　combined　with　a　self-learning　strategy　is　proposed　for　assembly　sequence　planning.　In　addition,　obstacle　avoidance　and　the　non-collision　constraints　between　workpieces　in　the　assembly　process　are　considered.　According　to　the　experiment　results,　the　new　method　is　superior　to　the　STOA　methods　in　terms　of　optimization　efficiency.　More　importantly,　the　proposed　method　has　satisfactory　generalization　ability　for　different　assembly　tasks.　Note　to　Practitioners-This　paper　studies　assembly　sequence　planning　problems　for　different　real-world　applications　in　industrial　and　home　service　fields.　Many　assembly　sequence　planning　solutions　have　been　widely　utilized　before.　However,　the　generalization　ability　of　the　previous　methods　is　not　satisfactory　since　the　re-adjust　process　is　required　when　the　workpiece　number　or　collision　condition　changes　in　different　tasks.　Motivated　by　the　above　reasons,　this　paper　develops　a　learning-based　assembly　sequence　planning　solution　to　resolve　complex　assembly　problems　without　parameter　re-adjustment　processes.　Users　can　directly　apply　the　developed　workpiece　identification　and　localization　method　to　obtain　the　sensing　information.　Then,　the　newly　designed　collision-free　cost　function　should　be　programmed　as　the　core　of　the　assembly　sequence　optimization.　Next,　the　proposed　neural　combinatorial　optimization　(NCO)　with　the　sensing　information　and　target　configuration　as　inputs　can　provide　the　optimal　assembly　sequence　by　self-learning.　The　learned　NCO-based　method　can　be　directly　applied　to　diverse　planning　tasks,　even　with　different　workpiece　numbers.　Users　can　also　refer　to　the　experimental　examples　in　this　paper　for　the　extension　of　the　proposed　method　to　their　own　applications.