Admittance　control　of　the　robot　is　an　important　method　to　improve　human-robot　collaborative　performance.　However,　it　displays　poor　matching　between　admittance　parameters　and　human-robot　collaborative　motion.　This　results　in　poor　motion　performance　when　the　robot　interacts　with　the　changeable　environment　(human).　Therefore,　to　improve　the　performance　of　human-robot　collaboration,　the　human-like　variable　admittance　parameter　regulator　(HVAPR)　based　on　the　change　rate　of　interaction　force　is　proposed　by　studying　the　human　arm＇s　static　and　dynamic　admittance　parameters　in　human-human　collaborative　motion.　HVAPR　can　generate　admittance　parameters　matching　with　human　collaborative　motion.　To　test　the　performance　of　the　proposed　HVAPR,　the　human-robot　collaborative　motion　experiment　based　on　HVAPR　is　designed　and　compared　with　the　variable　admittance　parameter　regulator　(VAPR).　The　satisfaction,　recognition　ratio,　and　recognition　confidence　of　the　two　admittance　parameter　regulators　are　statistically　analyzed　via　questionnaire.　Simultaneously,　the　trajectory　and　interaction　force　of　the　robot　are　analyzed,　and　the　performance　of　the　human-robot　collaborative　motion　is　assessed　and　compared　using　the　trajectory　smoothness　index　and　average　energy　index.　The　results　show　that　HVAPR　is　superior　to　VAPR　in　human-robot　collaborative　satisfaction,　robot　trajectory　smoothness,　and　average　energy　consumption.