This　paper　proposes　a　robotic　imitation　learning　method　which　integrates　the　deterministic　off-policy　reinforcement　learning　and　generative　adversarial　network.　This　method　allows　the　robot　to　implement　the　grasping　task　rapidly　by　learning　the　reward　function　from　the　demonstration　data.　Firstly,　the　discriminator　is　used　to　learn　the　reward　function　from　demonstrations,　which　can　guide　the　generator　to　complete　the　robot　grasping　task.　Secondly,　the　deep　deterministic　policy　gradient　method　is　used　as　the　generator　for　learning　action　policy　on　the　basis　of　discriminator.　In　particular,　the　demonstration　data　is　also　input　into　the　generator　to　ensure　its　performance.　Finally,　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　GAIL　method,　and　it　can　effectively　train　from　the　demonstration　data　in　different　states　of　the　task.　The　proposed　method　can　complete　the　robot　grasping　task　without　environmental　reward　quickly　and　improve　the　stability　of　the　training　process.　©　2018　IEEE