We are developing a surgical support system for a liver laparotomy surgery. In our system, we aim to navigate the surgical tools during surgery adapted to the change of the position, orientation and deformation of the liver during operation. The position and orientation of the liver is measured by matching the surface shape of the real liver measured by depth camera and the depth image generated from the 3D model of the liver. In this paper, we present a fast depth image generation method from computed tomography images (DICOM) using a general-purpose computing on graphics processing units (GPGPU). The experiments and results to estimate the position and orientation of the liver from the generated DICOM depth image and the depth image of a real liver model acquired from a depth camera are also described.

This paper proposes a novel shuffle-translating method that combines shuffle turns repeatedly for a humanoid robot. Conventionally, the walking motion of a humanoid robot is performed through a repeated foot stepping motion. However, this motion is inefficient and has low stability. Previously, we have studied the shuffle-turning method for a humanoid robot, which can perform a stepless and stable turning. In this paper, we present a precise shuffle turn with feedback control and a new shuffle translation method by repeating shuffle turns. Experiments using a humanoid robot were conducted and the results revealed that the proposed method was effective for shuffle translation.

In this study, grasp positions of human hands are measured for planar objects for development of a reasonable grasp criterion for a two-soft-fingered robotic hand. We first propose a grasp criterion that yields the best grasping position for a robot hand with two soft fingers. To examine the properties of this criterion with respect to some weighting coefficients included in the criterion, a grasping experiment is performed by humans for planar objects with various sizes and shapes. Results show that many observed grasping positions used by humans correspond to a set of weighting coefficients that equally weigh all the factors in the criterion. A grasping experiment using a robot has also been conducted using a set of coefficients corresponding to the equal weight; the effectiveness of the criterion was confirmed.

The purpose of this paper is to establish a method of shape recognition and grasping of unknown objects using a robotic hand with two soft fingers and one omnidirectional camera. For shape recognition, we propose to use a simple 2D version of the visual volume intersection method which takes advantage of a special feature of the omnidiretional camera attached to the hand. For grasping we propose a simple grasp quality criterion to obtain the best grasping position for the two soft fingers based on a visual hull in a horizontal grasp plane. We conducted several experiments and the results show the validity of the proposed method.

This paper proposes a method for evaluating quantitatively the degree of handedness of a person by using the haptic virtual reality technology. A definition is also given of a pair of quantitative indices for handedness and dexterity of a person from the data obtained by this method using the factor analysis. A haptic display system has been developed consisting of two PHANToM OMNI haptic devices, a graphics display, and a computer for virtual space calculation and control. To observe the skillfulness of two hands of a person, three virtual tasks have been developed: positioning task, force control task, and manipulation task. Ten male subjects aged from 22 to 23 years old have taken the test. The obtained performance data from these tasks have been analyzed using the factor analysis. Since the obtained factor scores for the right and left hands of each subject can be regarded as the skillfulness of the right and left hand, it is proposed to define the degree of handedness of the subject based on the difference of these factor scores. The proposed degree of handedness and the result of conventional LQ method did not contradict to each other. The calculated degrees of handedness, however, differ largely among the subjects. From this result, it is expected that the proposed method can be useful for detailed and quantitative evaluation of handedness.

In this paper, we introduce an annotation-based assistance system for an unmanned helicopter with a wearable augmented reality environment. In this system, an operator controls the helicopter remotely while watching an annotated view from the helicopter through a head mounted display (HMD) with a laptop PC in a backpack. Annotations assist the operation indicating some conditions of the helicopter and a name of buildings nearby. The position and the attitude of the helicopter is measured by GPS and a gyroscope, and sent to the operator's PC via a wireless LAN.

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.