This paper describes a robot-mediated information guide system that introduces audio-visual information to users interactively. The developed information guide system is composed of a touch panel interface, a mediator robot and backyard control systems. The touch panel interface functions to display audio-visual contents, while the mediator robot attracts people to this guide system and help user's operation. Many robots in literature are equipped with interactive audio-visual interface, however in most cases the audio-visual interface is a front end of the robot for presentation. We propose a new communication framework in which the robot behaves as a mediator to make a bridge between users and audio-visual interface by generating speeches and gestures. We hypothesize that entrainment of the mediator robot with users and contents could promote user's immersion in the contents. We conducted experiments with the proposed information guide system to examine the effect of robot mediation in a realistic interaction scenario that actually guides city sites for visitors. The developed information guide system was demonstrated in the science museum of the city in May 2014 and will be used for public events in future.

It is hypothesized that suitable forces and postures of digits for holding an object are planned simultaneously based on the same motor criterion, using an internal representation of the object. To demonstrate the computational potential of this hypothesis, a neural network model for learning and planning forces and postures of digits in object holding tasks is proposed. Integrating visual 2D images of 9 objects that are different in size and orientation with forces and postures of digits, the neural network internally represents the size and the orientation of the objects and memorizes 5 motor patterns per object. Moreover, the neural network restores forces and postures in natural holding tasks according to the same motor strategy in which a norm of forces of digits is minimized. These results support the computational plausibility of our hypothesis.

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.>