Robot　calligraphy　visually　reflects　the　motion　capability　of　robotic　manipulators.　While　traditional　researches　mainly　focus　on　image　generation　and　the　writing　of　simple　calligraphic　strokes　or　characters,　this　article　presents　a　generative　adversarial　network　(GAN)-based　motion　learning　method　for　robotic　calligraphy　synthesis　(Gan2CS)　that　can　enhance　the　efficiency　in　writing　complex　calligraphy　words　and　reproducing　classic　calligraphy　works.　The　key　technologies　in　the　proposed　approach　include:　(1)　adopting　the　GAN　to　learn　the　motion　parameters　from　the　robot　writing　operation;　(2)　converting　the　learnt　motion　data　into　the　style　font　and　realising　the　transition　from　static　calligraphy　images　to　dynamic　writing　demonstration;　(3)　reproducing　high-precision　calligraphy　works　by　synthesising　the　writing　motion　data　hierarchically.　In　this　study,　the　motion　trajectories　of　sample　calligraphy　images　are　firstly　extracted　and　converted　into　the　robot　module.　The　robot　performs　the　writing　with　motion　planning,　and　the　writing　motion　parameters　of　calligraphy　strokes　are　learnt　with　GANs.　Then　the　motion　data　of　basic　strokes　is　synthesised　based　on　the　hierarchical　process　of　＇stroke-radical-part-character＇.　And　the　robot　re-writes　the　synthesised　characters　whose　similarity　with　the　original　calligraphy　characters　is　evaluated.　Regular　calligraphy　characters　have　been　tested　in　the　experiments　for　method　validation　and　the　results　validated　that　the　robot　can　actualise　the　robotic　calligraphy　synthesis　of　writing　motion　data　with　GAN.