In order to find desired information on mobile phones, it is necessary to set search keywords easily and to explore a search if a user's information needs are not well defined. We propose an information navigation method to help users succeed in finding information on mobiles. Our proposal method presents to users a focus facet by analyzing context about contents, users and dialog. The focus facet is presented each time users refine a search. Users can select their desired keyword from the facet, and check the search results, and then users can define their ambiguous needs through interaction. We evaluated our method compared to a traditional search method. In our experiments, a user's load for setting some keywords of our method is lower than the traditional search method. Users can experience greater search success and satisfaction using our method than the traditional search method.

Robotics can serve as a testbed for cognitive theories. One behavioral criterion for comparing theories is the extent to which their implementations can learn to exploit new environmental opportunities. Furthermore, a robotics testbed forces researcher to confront fundamental issues concerning how internal representations are grounded in activity. In our approach, a mobile robot takes the role of a creature that must survive in an unknown environment. The robot has no a priori knowledge about what constitutes a suitable goal-what is edible, inedible, or dangerous-or even its shape or how its body works. Nevertheless, the robot learns how to survive. The robot does this by tracking segmented regions of its camera image while moving. The robot projects these regions into a canonical wavelet domain that highlights color and intensity changes at various scales. This reveals sensory invariance that is readily extracted with Bayesian statistics. The robot simultaneously learns an adaptable sensorimotor mapping by recording how motor signals transform the locations of regions on its camera image. The robot learn about its own physical extension when it touches an object. But it also undergoes an internal state change analogous to the thirst quenching or nausea producing effects of intake in animals. This allows the robot to learn what an object affords by relating these effects to learned clusters of invariance. In this way primitive symbols emerge. These protosymbols provide the robot with goals that it can achieve by using its sensorimotor mapping to navigate, for example, toward food and away from danger.

Robotics can serve as a testbed for cognitive theories. One behavioral criterion for comparing theories is the extent to which their implementations can learn to exploit new environmental opportunities. Furthermore, a robotics testbed forces researchers to confront fundamental issues concerning how internal representations are grounded in activity. In our approach, a mobile robot takes the role of a creature that must survive in an unknown environment. The robot has no a priori knowledge about what constitutes a suitable goal, what is edible, inedible, or dangerous, or even its shape or how its body works. Nevertheless, the robot learns how to survive. The robot does this by tracking segmented regions of its camera image while moving. The robot projects these regions into a canonical wavelet domain that highlights color and intensity changes at various scales. This reveals sensory invariance that is readily extracted with Bayesian statistics. The robot simultaneously learns an adaptable sensorimotor mapping by recording how motor signals transform the locations of regions on its camera image. The robot learns about its own physical extension when it touches an object, but it also undergoes an internal state change analogous to the thirst quenching or nausea producing effects of intake in animals. This allows the robot to learn what an object affords: is it edible or poisonous, by relating these effects to learned clusters of invariance. In this way primitive symbols emerge. These proto-symbols provide the robot with goals that it can achieve by using its sensorimotor mapping to navigate, for example, toward food and away from danger.

In the drive towards miniaturization in manufacturing, accuracy in positioning minute objects by camera is vital. For visual servoing, the rapid and robust detections of features in camera images is also essential to production line efficiency. Template matching provides flexibility in achieving this, often lacked by other methods, because it avoids the need to set object-specific parameters. Unfortunately, standard methods of template matching require much calculation, especially for detecting feature rotation. The delay this causes means that for many applications template matching provides too slow a source of visual feedback. As an alternative, we propose a new method of detecting the translation and rotation of a feature from coarse optical flow, we and apply it to visual servoing. Coarse optical flow is derived from the difference in intensity between a region of the initial and current image and their pixel-by-pixel intensity gradients. Unlike template matching, our method can detect large rotations with relatively little calculation. Image resolution is then adjusted from coarse to fine. Subpixel accuracy results in a 100 fold improvement in precision (by area). We show experimental results for precise planar positioning.