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During a human-exoskeleton collaboration, the interaction torque on exoskeleton resulting from the human cannot be clearly determined and conducted by normal physical models. This is because the torque depends not only on direction and orientation of both human-operator and exoskeleton but also on the physical properties of each operator. In this paper, we present our investigations on the relationship between the interaction torques with the dynamic factors of the human-exoskeleton systems using state-of-the-art learning techniques (nonparametric regression techniques) and provide control applications based on the findings. Experimental data was collected from various human-operators when they were attached to the designed exoskeleton to perform unconstraint motions with and without control. The results showed that regardless of how the experiments were done and which learning method was chosen, the resulting interaction could be best represented by time varying non-linear mappings of the operator's angular position, and the exoskeleton's angular position, velocity, and acceleration during locomotion. This finding has been applied to advanced controls of the lower exoskeletal robots in order to improve their performance while interacting with human.

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