The　achievement　of　robot　autonomy　has　environmental　perception　as　a　prerequisite.　The　hazards　rendered　from　uneven,　soft　and　slippery　terrains,　which　are　generally　named　non-geometric　hazards,　are　another　potential　threat　reducing　the　traversing　efficient,　and　therefore　receiving　more　and　more　attention　from　the　robotics　community.　In　the　paper,　the　vibration-based　terrain　classification　(VTC)　is　investigated　by　taking　a　very　practical　issue,　i.e.,　lack　of　labels,　into　consideration.　According　to　the　intrinsic　temporal　correlation　existing　in　the　sampled　terrain　sequence,　a　modified　Laplacian　SVM　is　proposed　to　utilise　the　unlabelled　data　to　improve　the　classification　performance.　To　the　best　of　our　knowledge,　this　is　the　first　paper　studying　semi-supervised　learning　problem　in　robotic　terrain　classification.　The　experiment　demonstrates　that:　(1)　supervised　learning　(SVM)　achieves　a　relatively　low　classification　accuracy　if　given　insufficient　labels;　(2)　feature-space　homogeneity　based　semi-supervised　learning　(traditional　Laplacian　SVM)　cannot　improve　supervised　learning＇s　accuracy,　and　even　makes　it　worse;　(3)　feature-　and　temporal-space　based　semi-supervised　learning　(modified　Laplacian　SVM),　which　is　proposed　in　the　paper,　could　increase　the　classification　accuracy　very　significantly.