This　paper　proposes　a　deterministic　generative　adversarial　imitation　learning　method　which　allows　the　robot　to　implement　the　motion　planning　task　rapidly　by　learning　from　the　demonstration　data　without　reward　function.　In　our　method,　the　deep　deterministic　policy　gradient　method　is　used　as　the　generator　for　learning　the　action　policy　on　the　basis　of　discriminator,　and　the　demonstration　data　is　input　into　the　generator　to　ensure　its　stability.　Three　experiments　on　the　push　and　pick-and-place　tasks　are　conducted　in　the　gym　robotic　environment.　Results　show　that　the　learning　speed　of　our　method　is　much　faster　than　the　stochastic　generative　adversarial　imitation　learning　method,　and　it　can　effectively　learn　from　the　demonstration　data　in　different　states　of　the　task　with　higher　learning　stability.　The　proposed　method　can　complete　the　motion　planning　task　without　environmental　reward　quickly　and　improve　the　stability　of　the　training　process.　(C)　2020　Elsevier　B.V.　All　rights　reserved.