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In this study, we present a new type-1 fuzzy logic-based controller for push recovery by humanoids. The objective of this study is to develop an intelligent controller and to implement biologically inspired push recovery for humanoid robots. The fuzzy inference system takes two crisp values as inputs, which are fuzzified, before a number of rules are applied, and finally the output is defuzzified to convert it into a crisp value. We apply fuzzy rules to our model, which we simulate in an unstructured environment. The objective is to reduce the fuzzy rules and make the fuzzy inference set less computationally intensive and fast, as well as exploiting the advantages of easy trainability and high generalizability. The fuzzy logic-based controller can predict the requisite push recovery strategy and whether the robot will be able to recover or fall. The architecture has a hierarchical design. The first fuzzy inference system (FIS1) is …