Robotic　systems　frequently　need　to　plan　consecutive　similar　manipulation　in　some　scenarios　(e.g.,　pick-and-place　tasks),　leading　to　similar　motion　plans.　Moreover,　the　workspace　of　a　robot　changes　with　the　difference　in　operation　actions,　which　affects　subsequent　tasks.　Therefore,　it　is　significant　to　reuse　information　from　previous　solutions　for　new　motion　planning　instances　to　adapt　to　workplace　changes.　This　paper　proposes　the　Lazy　Demonstration　Graph　(LDG)　planner,　a　novel　motion　planner　that　exploits　successful　and　high-quality　planning　cases　as　prior　knowledge.　In　addition,　a　Gaussian　Mixture　Model　(GMM)　is　established　by　learning　the　distribution　of　samples　in　the　planning　cases.　Through　the　trained　GMM,　more　samples　are　placed　in　a　promising　location　related　to　the　planning　tasks　for　achieving　the　purpose　of　adaptive　sampling.　This　adaptive　sampling　strategy　is　combined　with　the　Lazy　Probabilistic　Roadmap　(LazyPRM)　algorithm;　in　the　subsequent　planning　tasks,　this　paper　uses　the　multi-query　property　of　a　road　map　to　solve　motion　planning　problems　without　planning　from　scratch.　The　lazy　collision　detection　of　the　LazyPRM　algorithm　helps　overcome　changes　in　the　workplace　by　searching　candidate　paths.　Our　method　also　improves　the　quality　and　success　rate　of　the　path　planning　of　LazyPRM.　Compared　with　other　state-of-the-art　motion　planning　algorithms,　our　method　achieved　better　performance　in　the　planning　time　and　path　quality.　In　the　repetitive　motion　planning　experiment　of　the　manipulator　for　pick-and-place　tasks,　we　designed　two　different　experimental　scenarios　in　the　simulation　environment.　The　physical　experiments　are　also　carried　out　in　AUBO-i5　robot　arm.　Accordingly,　the　experimental　results　verified　our　method＇s　validity　and　robustness.