We　explore　the　problem　of　human-like　motion　planning　for　robotic　arms　based　on　researches　in　biophysics,　robotics　and　motion　planning.　Two　common　manipulation　tasks,　reaching　point　movement　in　obstacle　free　environment　and　reaching　point　movement　through　a　narrow　passage,　are　separately　studied.　First,　we　proposed　a　hypothesis　named　＇Target　Arm　Pose＇　(TAP)　to　interpret　the　natural　motion　of　human　arm.　Second,　based　on　TAPs　and　the　minimum　jerk　model,　we　develop　a　new　robotic　inverse　kinematic　algorithm　in　joint　jerk　level　to　conduct　the　reaching　point　movement　in　obstacle　free　environment.　Furthermore,　based　on　the　new　algorithm　we　utilize　the　redundancy　of　the　robot　to　avoid　joint　velocity　limits.　Next,　we　use　existing　Bi-RRT　algorithm　combined　with　TAPs　to　implement　the　reaching　point　movement　through　a　narrow　passage.　Finally,　comparison　between　these　two　tasks　and　two　algorithms　is　made　to　give　a　systematic　analysis　on　the　human-like　motion　planning　problem.　©　2011　IEEE.