Imitation　learning　is　a　significant　means　of　human　learning,　but　also　the　main　research　field　in　the　mechanism　of　bionic　robot.　This　paper　focuses　on　the　imitation　learning　strategies　of　robot　in　the　framework　of　probabilistic　model.　Discrete　teaching　data　are　used　as　training　samples　of　Gaussian　process　to　acquire　demonstration　trajectory.　RBF　neural　network　is　adopted　to　express　imitation　control　strategy.　The　imitation　trajectory　with　imitation　control　strategy　which　contains　unknown　parameters　is　modeled　by　Gaussian　process.　KL　divergence　is　constructed　with　the　probability　distribution　of　demonstration　and　imitation　trajectory,　and　gradient　descent　method　is　used　to　minimize　the　KL　divergence　so　as　to　seek　the　optimal　strategy　of　imitation.　Then　the　imitation　task　is　learned　gradually　by　updating　the　optimal　strategy　to　imitative　robot.　The　swing　behavior　of　the　articulated　robot　arm　is　used　as　the　simulation　task　of　imitation　learning,　and　the　result　of　simulation　experiments　demonstrates　the　effectiveness　of　the　robotic　control　strategy　for　imitation　learning　based　on　KL　divergence　and　RBF　neural　network.　©　2015　IEEE.