In this thesis, an adaptive leading behavior model for a biometric robot is proposed to lead a guppy: A series of analyses of fish shoals has been performed to provide the theoretical support for this model. This model uses quantified reactions of fish to the robot as feedback. It is implemented using a state machine. The behavior of this model has been divided into sub-behaviors, while each sub-behavior corresponds to a state of the state machine. The adjustment of the robot's motion is made within most of the state and the transitions of the state dependent on the environment in real time. The results indicate that this model is effective to lead a live guppy.