Embedding task-based behavior into internal sensory-based attractor dynamics in navigation of a mobile robot

This study shows a novel approach for learning sensory- and task-based navigation of a mobile robot. The learning of task-based behavior is formulated as an embedding problem of dynamical systems: the desired trajectories in a task space should be embedded into an adequate sensory-based internal state space so that unique mapping from the internal state space to the motor command can be established. We discuss how such internal state space and its mapping can be self-organized by means of neural learning methodologies. Physical experiment by a mobile robot with laser range sensor was conducted, which showed that learning of homing and cyclic routing tasks were successfully realized on two different neural network (NN) architectures; the time delayed type and the recurrent type. The navigation was inherently robust against miscellaneous noise since those tasks were embedded in the global attractor dynamics of a fixed point and limit cycling