Spiking neurons might play a larger role than simply as an efficient signal transmitter. Several studies have demonstrated how movements can be generated using networks of spiking neurons. However, the complexity of spiking neural networks makes their implementation difficult, and the use of spiking neurons in robotics has remained largely impractical. In this paper, we show that the addition of a single layer of spiking neurons can help improve performance on stabilization tasks in dynamically changing environments. In a one-dimensional inverted pendulum stabilization task, the spiking neurons seem to expand the space of usable parameters of the controller. Using a robot arm in 3-D space, the additional layer of spiking neurons suffices to improve performance up to 30% on an inverted pendulum stabilization task. We expect this technique to enhance performance in most stabilization tasks but also tasks that are essentially similar such as reaching tasks and posture control. We also expect the effects of this layer to be greatest when the optimal tuning of control parameters is difficult, such as when the environment is unpredictable and dynamic.

It is essential for a monitoring system or a communication robot that interacts with an elderly person to accurately understand the user's state and generate actions based on their condition. To ensure elderly welfare, quality of life (QOL) is a useful indicator for determining human physical suffering and mental and social activities in a comprehensive manner. In this study, we hypothesize that visual information is useful for extracting high-dimensional information on QOL from data collected by an agent while interacting with a person. We propose a QOL estimation method to integrate facial expressions, head fluctuations, and eye movements that can be extracted as visual information during the interaction with the communication agent. Our goal is to implement a multiple feature vectors learning estimator that incorporates convolutional 3D to learn spatiotemporal features. However, there is no database required for QOL estimation. Therefore, we implement a free communication agent and construct our database based on information collected through interpersonal experiments using the agent. To verify the proposed method, we focus on the estimation of the "mental health" QOL scale, which is the most difficult to estimate among the eight scales that compose QOL based on a previous study. We compare the four estimation accuracies: single-modal learning using each of the three features, i.e., facial expressions, head fluctuations, and eye movements and multiple feature vectors learning integrating all the three features. The experimental results show that multiple feature vectors learning has fewer estimation errors than all the other single-modal learning, which uses each feature separately. The experimental results for evaluating the difference between the estimated QOL score by the proposed method and the actual QOL score calculated by the conventional method also show that the average error is less than 10 points and, thus, the proposed system can estimate the QOL score. Thus, it is clear that the proposed new approach for estimating human conditions can improve the quality of human-robot interactions and personalized monitoring.

Quality of life (QoL) is an effective index of well-being, including physical health, aspect of social activity, and mental state of individuals. A new approach that uses a deep-learning architecture to estimate the score of a user's QoL is presented. This system was built using a combination of a 3D convolutional neural network and a support vector machine for multimodal data. In order to evaluate the accuracy of the estimation system, three experiments were conducted. Before these experiments, ten hours of audio and video data were collected from healthy participants during a natural-language conversation with a conversational agent we implemented. In the first experiment, the QoL question-answer estimation experiment, the accuracy of “Physical functioning,” which is one of the eight scales that constitute QoL, reached 84.0%. In the second experiment, the QoL-score-regression experiment, in which the scores of each scale were directly estimated, the distribution of the difference between the actual score and the estimated results, known as error, was investigated. These results imply that the features necessary for QoL estimation can be extracted from audio and video data, except for the “Mental Health” domain. One of the reasons why it was difficult to estimate the “Mental Health” scale may be that the learning framework could not extract an appropriate feature for estimation. Therefore, we estimated “Mental Health” by focusing on eye movement. From the result, it was proven that estimation is possible, and the proposed system using multimodal data demonstrated its effectiveness for estimation for all eight scales that constitute QoL and for extracting high-dimensional information regarding the QoL of a human, including their satisfaction level towards daily life and social activities. Finally, suggestions and discussions regarding the plausible behavior of the estimation results were made from the viewpoint of human–agent interaction in the field of elderly welfare.

A spherical mobile robot has been developed with a 1-DOF mechanism that can generate two motions, translational and rotational, using only one motor. The basic concept is to attach vinyl strips to the sphere at regular intervals and to change the driving modes of the motor to produce the desired motion. For example, placing the motor in vibration mode causes the trajectory of the contact point between the sphere and ground to follow a specified closed path, such as an elliptical path, which makes the sphere rotate against the ground. Testing of this concept using a self-developed simulator confirmed that rotational motion is generated by the interactions between the surface attachments and the ground. On the basis of these results, a prototype spherical mobile robot was developed, and testing confirmed that switching the motor driving mode generated two different motions.

In this research, we developed a sphere-shaped mobile robot that can generate multiple behaviors by using only one motor. The robot can generate the translational motion and the rotational motion by controlling the motion of the motor. The motor itself acts as an eccentric weight during motions. To generate emergent behaviors, many protrusions are mounted on the surface of the spherical body. The emergent behaviors occur by an interaction between the external world and these protrusions when the sphere is vibrating, and the robot can move in a random walk manner.

In this paper, synthetic research for primevaloids, robots of primitive appearance, is described. First, we briefly introduce goals of the project within a perspective of entertainment robotics. Next, we sketch design concepts and prospective principles for a novel kind of entertainment robots, while placing our emphasis on primeval appearance, survivability, and emotions. Then, after introducing several appearance designs of a robot, we shift our focus to a locomotor for them. After we simplified their appearances into a skeletal model, we implement a simple embodied agent within a physical simulation. Using the physical simulation, we test the validity of the locomotion model, and analyze basic behaviors of the agent. We also test swing-slip locomotion within the real-world. It is shown that swinging creature’s head leads to stable locomotion.

In this paper, characteristics of a sensory-motor system driven by coupled bursting neurons are examined. We constructed a sensory-motor system by connecting networks of coupled bursting neurons. Mutually connected networks showed several kinds of oscillation rhythms. When networks oscillate at 5-10 Hz, a robot pushed an object in an environment, and when networks oscillate at 20-50 Hz, a robot avoided an object. Spontaneous transition between these rhythms was observed, and was clarified to be an emergent property based on cooperative phenomena of bursting neurons. These studies may provide a basis for utilization of coupled bursting neurons and its primitive applications.