This　paper　proposed　a　high　speed　learning　from　demonstrations　(LfD)　method　for　sparse　reward　based　motion　planning　problem　of　manipulator　by　using　hindsight　experience　replay　(HER)　mechanism　and　deep　deterministic　policy　gradient　(DDPG)　method.　First,　a　demonstrations　replay　buffer　and　an　agent　exploration　replay　buffer　are　created　for　storing　experience　data,　and　the　hindsight　experience　replay　mechanism　is　subsequently　used　to　acquire　the　experience　data　from　the　two　replay　buffers.　Then,　the　deep　deterministic　policy　gradient　method　is　used　to　learn　the　experience　data　and　finally　fulfil　the　manipulator　motion　planning　tasks　under　the　sparse　reward.　Last,　experiments　on　the　pushing　and　pick-and-place　tasks　were　conducted　in　the　robotics　environment　in　the　gym.　Results　show　that　the　training　speed　is　increased　to　at　least　10　times　as　compared　to　the　deep　deterministic　policy　gradient　method　without　demonstrations　data.　In　addition,　the　proposed　method　can　effectively　utilize　the　sparse　reward,　and　the　agent　can　quickly　complete　the　task　even　under　the　low　success　rate　of　demonstrations　data.　©　2018　IEEE.