Hisashi Ishihara

Realistic child robot “Affetto” for understanding the caregiver-child attachment relationship that guides the child development

Children are considered to develop various kinds of their social abilities in communication with their caregivers. Developmental researchers have revealed the quality of the caregiver-child attachment heavily affects the childrens developmental passway and sometimes threatens their healthy development. For understanding developmental mechanism under the caregiver-child attachment, a number of theoretical models have been proposed and some child robots have been created to test these models or to find new facts about development. However, most of these robots have not been provided with a realistic childlike appearance or facial expressions, which seem important to induce caregivers attachment. Since what kinds of treatment robots receive from the “caregivers” appears to depend on what kinds of impression the robots give to their caregivers, more realistic robot that is more close to a real child seems needed. In this paper, we introduce our project to build a new child robot, Affetto, that has realistic appearance of 1- to 2-year-old child, and discuss what kinds of issues on child development can be examined by using it.

How Caregiver's Anticipation Shapes Infant's Vowel Through Mutual Imitation

The mechanism of infant vowel development is a fundamental issue of human cognitive development that includes perceptual and behavioral development. This paper models the mechanism of imitation underlying caregiver-infant interaction by focusing on potential roles of the caregivers imitation in guiding infant vowel development. Proposed imitation mechanism is constructed with two kinds of the caregivers possible biases in mind. The first is what we call ¿sensorimotor magnets,¿ in which a caregiver perceives and imitates infant vocalizations as more prototypical ones as mother-tongue vowels. The second is based on what we call ¿automirroring bias,¿ by which the heard vowel is much closer to the expected vowel because of the anticipation being imitated. Computer simulation results of caregiver-infant interaction show the sensorimotor magnets help form small clusters and the automirroring bias shapes these clusters to become clearer vowels in association with the sensorimotor magnets.

Design of 22-DOF pneumatically actuated upper body for child android ‘Affetto’

Child robots have been used in a lot of studies on human–robot social/physical interaction because they are suitable for safe and casual experiments. However, providing many compliant joints and lifelike exteriors to enhance their interaction potential is difficult because of the limited space available inside their bodies. In this paper, we propose an upper body structure that consists of slider crank and parallel mechanisms for linear actuators and serial mechanisms for rotary actuators. Such combinations of several joint mechanisms efficiently utilize the body space; in total, 22 degrees of freedoms were realized in an upper body space equivalent to that of an 80cm tall child. A pneumatic drive system was adopted in order to fully verify the behavioral performance of the body mechanism. The proposed redundant and compact upper body mechanism can be a platform for testing the effectiveness of future exteriors for the little child android ‘Affetto’, which was developed in order to integrate several key characteristics for achieving advanced human–robot interaction. Graphical Abstract

Caregiver’s sensorimotor magnets lead infant’s vowel acquisition through auto mirroring

Mother-infant vocal communication is a sort of mystery of human cognitive development since they can communicate although their body structures and therefore their utterable areas are different. This paper proposes a method that aids unconscious guidance in mutual imitation for infant development based on a biasing element with two different kinds of modules. The first is based on the normal magnet effect in perceiving heard vocal sounds as the listenerpsilas own vowels (perceptual magnet) and also includes another magnet effect for imitating vocal sounds that resemble the imitatorpsilas vowels (articulatory magnet). The second is based on what we call ldquoauto mirroring bias,rdquo by which the heard vowel is much closer to the expected vowel because the otherpsilas utterance is an imitation of the listenerpsilas own utterance. Computer simulation results of mother-infant interaction show the validity of the proposed bias. Finally future issues are discussed.

Design of an Articulation Mechanism for an Infant-like Vocal Robot “Lingua”

Spoken language is one of the important means for humans to communicate with others. In developmental psychology, it is suggested that an infant develops it through verbal interaction with caregivers by observation experiments [1]. However, what kind of underlying mechanism works for that and how caregiver’s behavior affects on this process has not been fully investigated yet since it is very difficult to control the infant vocalization. On the other hand, there are several constructive approaches to understand the mechanisms by using infant robots with abilities equivalent to those of human infants, as a controllable platform [2].

Mexican-Hat-Like Response in a Flexible Tactile Sensor Using a Magnetorheological Elastomer

A significant challenge in robotics is providing a sense of touch to robots. Even though several types of flexible tactile sensors have been proposed, they still have various technical issues such as a large amount of deformation that fractures the sensing elements, a poor maintainability and a deterioration in the sensitivity caused by the presence of a thick and soft covering. As one solution for these issues, we proposed a flexible tactile sensor composed of a magnet, magnetic transducer and dual-layer elastomer, which consists of a magnetorheological and nonmagnetic elastomer sheet. In this study, we first investigated the sensitivity of the sensor, which was found to be high (approximately 161 mV/N with a signal-to-noise ratio of 42.2 dB); however, the sensor has a speed-dependent hysteresis in its sensor response curve. Then, we investigated the spatial response and observed the following results: (1) the sensor response was a distorted Mexican-hat-like bipolar shape, namely a negative response area was observed around the positive response area; (2) the negative response area disappeared when we used a compressible sponge sheet instead of the incompressible nonmagnetic elastomer. We concluded that the characteristic negative response in the Mexican-hat-like response is derived from the incompressibility of the nonmagnetic elastomer.

Derivation of simple rules for complex flow vector fields on the lower part of the human face for robot face design

It is quite difficult for android robots to replicate the numerous and various types of human facial expressions owing to limitations in terms of space, mechanisms, and materials. This situation could be improved with greater knowledge regarding these expressions and their deformation rules, i.e. by using the biomimetic approach. In a previous study, we investigated 16 facial deformation patterns and found that each facial point moves almost only in its own principal direction and different deformation patterns are created with different combinations of moving lengths. However, the replication errors caused by moving each control point of a face in only their principal direction were not evaluated for each deformation pattern at that time. Therefore, we calculated the replication errors in this study using the second principal component scores of the 16 sets of flow vectors at each point on the face. More than 60% of the errors were within 1 mm, and approximately 90% of them were within 3 mm. The average error was 1.1 mm. These results indicate that robots can replicate the 16 investigated facial expressions with errors within 3 mm and 1 mm for about 90% and 60% of the vectors, respectively, even if each point on the robot face moves in only its own principal direction. This finding seems promising for the development of robots capable of showing various facial expressions because significantly fewer types of movements than previously predicted are necessary.

Path Analysis for the Halo Effect of Touch Sensations of Robots on Their Personality Impressions

Physical human–robot interaction plays an important role in social robotics, and touch is one of the key factors that influences human’s impression of robots. However, very few studies have explored different conditions, and therefore, few systematic results have been obtained. As the first step toward addressing this issue, we studied the types of impressions of robot personality that humans may experience when they touch a soft part of a robot. In the study, the left forearm of a child-like android robot “Affetto” was exposed; this forearm was made of silicone rubber and can be replaced with one of other three forearms providing different sensations of hardness upon touching. Participants were asked to touch the robot’s forearm and to fill evaluation questionnaires on 19 touch sensations and 46 personality impressions under each of four conditions with different forearms. Four impression factors for touch sensations and three for personality impressions were extracted from the evaluation scores by the factor analysis method. The causal relationships between these factors were analyzed by the path analysis method. Several significant causal relationships were found, for example, between preferable touch sensations and likable personality impressions. The results will help design robots’ personality impression by designing touch sensations more systematically.

Compliant and Compact Joint Mechanism for a Child Android Robot

We have developed a compact joint mechanism for a child android robot “Affetto”, which is designed for more intuitive and close physical human-robot interaction. The joint mechanism has 26 DOFs (3 in neck, 1 in chest, 2 in waist, 5 in each arm, 5 in each leg) in its latest version. All of them are driven by pneumatic actuators (air cylinders and air vane actuators), which provide them with compliance and agility. Such flexible and compact joint mechanism will enable safe and casual physical interaction with appropriate soft coverings our project team is now developing.

Investigation of Causal Relationship Between Touch Sensations of Robots and Personality Impressions by Path Analysis

Physical human–robot interaction plays an important role in social robotics, and touch is one of the key factors that influence a human’s impression of robots. However, very few studies have explored different conditions pertaining to such interactions, and therefore, only few systematic results have been reported. As the first step toward addressing this issue, we studied the types of impressions of a robot’s personality that humans may experience when they touch a soft part of the robot. In the study, the left forearm of a child-like android robot named “Affetto” was exposed; the original forearm was made of silicone rubber and can be replaced with any one of three other forearms that provide different sensations of hardness upon touching. Participants were asked to touch the robot’s forearm and answer evaluation questionnaires on 19 touch sensations and 46 personality impressions for each of the four conditions associated with the different forearms. Four impression factors for touch sensations and three for personality impressions were extracted from the evaluation scores by the factor analysis method. The causal relationships between these factors were analyzed by the path analysis method. Several significant causal relationships were found, for example, between preferable touch sensations and likable personality impressions. The study results are expected to help in the design of the personality impressions of robots by facilitating a more systematic design of touch sensations. Furthermore, the results of an analysis of variance suggested that the most preferable touch sensation is possibly related to the robot’s appearance and that designers of social robots should understand the complex touch sensation properties of humans.