The　multimedia　has　achieved　dominant　positions　in　both　local　storage　and　internet　bandwidth,　which　inevitably　promotes　the　compression　of　audio,　image　and　video　information.　Nowadays,　the　emerging　haptic　technology,　which　enhances　the　immersion　in　virtual　reality　and　remote　control,　has　also　brought　new　challenges　in　its　codec　design.　It　is　thus　imperative　to　develop　haptic　codecs,　including　kinesthetic　and　vibrotactile　codecs,　with　high　efficiency　and　low　delay.　In　this　paper,　we　exploit　statistical　features　of　vibrotactile　data　to　develop　a　Recurrent-Network-based　Vibrotactile　Codec　(RNVC)　with　high　compression　efficiency　and　low　coding　delay.　The　proposed　encoder　consists　of　vibrotactile　estimation　by　Gate　Recurrent　Unit　(GRU),　non-uniform　quantization/compensation　of　residuals　and　an　entropy　encoder.　In　particular,　the　GRU-based　recurrent　network　is　utilized　for　its　high　efficiency　to　predict　signals　and　low　complexity　to　converge.　The　decoder　consists　of　all　counterparts　of　encoder.　Experimental　results　show　the　proposed　RNVC　significantly　reduces　of　original　bitrates　with　negligible　encoding　delay,　which　achieves　the　state-of-the-art　coding　performance　of　vibrotactile　signal.