Robot　manipulation　is　one　of　prerequisites　capability　for　service　robot.　However,　autonomous　manipulation　remains　a　challenging　problem　for　robot　to　implement　the　task,　where　robot　has　physical　interactions　and　mechanical　contacts　with　its　environment.　To　date,　learning　from　demonstration　(LFD)　has　been　successfully　applied　to　enable　robot　to　acquire　new　manipulation　skill.　Researches　on　LFD　mainly　focus　on　representing　movement　trajectory　from　demonstration　and　then　transferring　the　new　reproduced　trajectory　to　robot.　In　this　paper,　a　learning　framework　is　introduced　to　learn　compliant　behavior　from　human　demonstration　and　transferring　it　to　robot.　Multivariable,　position　and　interaction　force,　will　be　simultaneously　encoded　in　a　probability　model.　The　control　mode　of　each　axis　in　C-frame　is　estimated　to　decouple　the　position　and　force　control.　And　an　external　DMP　is　presented　to　reproduce　the　new　desired　position　or　force　for　new　situations.　Furthermore,　the　compliant　parameters　are　analyzed　and　estimated　by　combining　probability　modeling　approach　and　dynamic　system　approach.　After　human　compliant　behavior　learning,　a　hybrid　external　position/force　control　is　presented　to　enable　robot　to　produce　a　human-like　compliant　behavior.　Experiments　are　performed　to　demonstrate　the　effectiveness　of　the　presented　learning　framework.　©　2016　IEEE.