Kazuhiko Shinozawa

This paper presents the effect of a robot's active touch for improving people's motivation. For services in the education and healthcare fields, a robot might be useful for improving the motivation of performing such repetitive and monotonous tasks as exercising or taking medicine. Previous research demonstrated with a robot the effect of user touch on improving its impressions, but they did not clarify whether a robot's touch, especially an active touch, has enough influence on people's motive. We implemented an active touch behavior and experimentally investigated its effect on motivation. In the experiment, a robot requested participants to perform a monotonous task with a robot's active touch, a passive touch, or no touch. The result of experiment showed that an active touch by a robot increased the number of working actions and the amount of working time for the task. This suggests that a robot's active touch can support people to improve their motivation. We believe that a robot's active touch behavior is useful for such robot's services as education and healthcare.

This paper describes a method for recognizing the environment and selecting behaviors for a humanoid robot that are appropriate to the environment, based on sensor histories. In order for humanoid robots to move around many kinds of environments, one of the keys is selecting the appropriate behavior. Because features of an environment that deeply influence a robot's motion, such as viscous friction or floor elasticity, are difficult to immediately measure before moving, it is difficult for the robots to plan their behaviors to take these factors into account. Therefore, in our method, a robot first records a long time series of data from sensors attached to its body while performing each behavior in each environment, and it builds decision trees based on this data for each of the behaviors, enabling it to recognize the current environment based on the sensor history. By using the trees, the robot selects the behavior which will be most effective for recognizing the environment, acquires a sensor history while doing the behavior, and selects likely candidates for the environment. By iterating over these steps, the robot can recognize the environment and select an appropriate behavior. To verify the validity of the method, we used a small-size humanoid robot and conducted an experiment to recognize environments in a family's house. © 2006 IEEE.

In this paper, we propose a method for translating human gestures to those of various robots that have different degrees and arrangements of freedom. A key idea of the method is to simplify human gesture by using relevant movements of two body parts while making a gesture and explicitly reducing the gesture's degrees of freedom by utilizing principle component analysis. Experiments were conducted with three types of different types of robots to confirm that this method could produce similar motions to human gestures. The results show that subjects had the impression that the produced motions appeared similar to the motions that humans make with the hand.