A novel approach to human-robot collaboration based on quasi-symbolic expressions is proposed. The target task is navigation in which a person with his or her covered and a humanoid robot collaborate in a context-dependent manner. The robot uses a recurrent neural net with parametric bias (RNNPB) model to acquire the behavioral primitives, which are sensory-motor units, composing the whole task. The robot expresses the PB dynamics as primitives using symbolic sounds, and the person influences these dynamics through tactile sensors attached to the robot Experiments with six participants demonstrated that the level of influence the person has on the PB dynamics is strongly related to task performance, the person's subjective impressions, and the prediction error of the RNNPB model (task stability). Simulation experiments demonstrated that the subjective impressions of the correspondence between the utterance sounds (the PB values) and the motions were well reproduced by the rehearsal of the RNNPB model.