In this paper, a strategy for adapting the NAO robot to different floors with different slipperiness degrees is presented while following a desired human-like Zero Moment Point trajectory. The robot's gait is generated based on the HRSP software package and it aims to be as human-like as possible. The gait parameters such as the step length and walking speed are optimized in order to generate gaits with adequate RCoF for the floor's ACoF, which minimizes the slipping probability, and as such

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... iding undesired falls. This choice of gait parameters is based on the analysis of the ground reaction forces and human behavior. Also, gait adaptation is further improved in order to follow a desired Zero Moment Point trajectory, through the use of a controller that offsets the hip and ankle joint angles. The novelty of this work lies on the fact that machine learning techniques are used to adapt the gait parameters and joint corrections to make the robot more resistant to both slipping and external disturbances.