In the pursuit of enhancing the quality of life, research in domestic robotics is rapidly evolving, offering a broad spectrum of services that not only provide practical aid but also contribute to emotional well-being and social interaction. However, existing domestic robots often lack essential features necessary for continuous interaction with humans and their surroundings, such as stability, mobility, safety, size, and physical interaction capabilities. Addressing this need, this paper introduces the development of a prototype robot tailored for harmonious coexistence with humans. This robot utilizes its own heavy equipment, such as batteries and actuators, to achieve an intrinsically balanced structure along with a comprehensive set of features optimized for seamless integration into human-centric environments. Focusing on the mechanical design of the robot, we evaluated its performance through a series of experiments, highlighting its mechanical advantages, addressing its limitations, and proposing solutions to enhance its performance for future applications.

Conversational recommendation systems are crucial for making recommendations agreeable to the user. To reach an agreeable recommendation, this study proposes a dialog strategy that represents a reasonable order of items and elicits the current estimation by the wording of utterances based on the subjective preference estimation. We developed two dialog functions for the topic and word choice based on the history of preference estimations. The human impression of a robot’s diligence, understanding capability, and satisfaction were evaluated through a conversation with a virtual robot using a crowdsourcing platform. We compared six conditions that differed based on two topics and three wording patterns. The experimental results indicated that the main effect of the wording patterns, whereas one of the topic choices was not found to be significant. Further analysis showed that accurate estimation improves the robot’s impression when demonstrating its diligence.

This paper presents an autonomous conversational mobile robot Butsukusa that can describe its own observations and internal states during patrolling tasks. The proposed robot can observe the surrounding environment using the recognition module for objects, humans, environment, localization, and speech and then move autonomously around an indoor living space. Interaction skills via language are required for the robot to perform in such human-centered spaces. To investigate a better communication protocol with users, we evaluate various language generation patterns based on different observations and interaction patterns. The evaluation results indicate that the importance of describing the robot's observation results and internal states, as well as the necessity of an appropriate description, depends on the situation.

IEEE 802.11ax, which was designed to improve effective throughput in dense environments, is expected to improve communication performance in campus wireless LANs serving classroom buildings and other areas containing densely concentrated access points. However, stable operation will require careful consideration of how overall throughput is affected by channel-bonding configurations and coexistence with older wireless networking standards. To this end, in the present study, we prepared multiple terminals and used iperf3 to measure throughput for various channel widths and in coexistence with older standard, obtaining insight into the optimal deployment of 802.11ax in campus wireless LANs. Our experimental results include the following findings: (1) When the number of simultaneously connecting terminals is small—on the order of 2 or 3— and electromagnetic interference from the surrounding environment is minimal, the use of 40-MHz channel bonding may improve overall throughput. (2) The coexistence with IEEE 802.11ac, overall throughput should improve as the ratio of 802.11ax terminals increases.

For a safe, natural and effective human-robot social interaction, it is essential to develop a system that allows a robot to demonstrate the perceivable responsive behaviors to complex human behaviors. We introduce the Multimodal Deep Attention Recurrent Q-Network using which the robot exhibits human-like social interaction skills after 14 days of interacting with people in an uncontrolled real world. Each and every day during the 14 days, the system gathered robot interaction experiences with people through a hit-and-trial method and then trained the MDARQN on these experiences using end-to-end reinforcement learning approach. The results of interaction based learning indicate that the robot has learned to respond to complex human behaviors in a perceivable and socially acceptable manner.

In this paper, we present results of investigation on the dynamics of group decision making - how people discuss and make a decision-with collaborative web search. Prior works proposed systems that support group decision making with web search but have not examined the influence of discussion behaviors especially on the satisfaction levels with the final conclusion. In this study, we conducted a set of experiments to observe discussion behaviors and the consequent satisfaction with the conclusion using our experimental system and a set of questionnaires. The task for each participant was to make a decision on a restaurant. Our primary results revealed (1) the similar activities across all groups at the beginning and the end of the group discussion, (2) a lack of correspondence between the satisfaction with the conclusion and the time spent to reach the conclusion, and (3) the presumption that a member who actively engaged in the activities that were visible for the other members was likely to be voted as a leader in the group discussion beyond the discussion. Finally, we discussed how to implement intelligent systems that aid group decision making.

In this paper, we propose a novel energy-efficient architecture for software-defined data center infrastructures. In our proposed data center architecture, we include an exhaust heat reuse system that utilizes high-temperature exhaust heat from servers in conditioning humidity and air temperature of office space near the data center. To obtain high-temperature exhaust heat, equipment such as server racks and air conditioners are deployed in tandem so that the aisles are divided into three types: cold, hot, and super-hot. In this paper, to investigate the fundamental characteristics of our proposed data center architecture, we consider various types of data center models and conduct numerical simulations that use results obtained by experiments at an actual data center. Through simulation, we show that the total power consumption by a data center with our proposed architecture is 27 percent lower than that by data center with a conventional architecture. In addition, it is also shown that the proposed tandem equipment arrangement is suitable for obtaining high-temperature exhaust heat and decreasing the total power consumption significantly under a wider range of conditions than in the conventional equipment arrangement.

We propose a model for predicting the temperature distribution in data centers by using a convolutional neural network (CNN). Changes in the temperature distribution depend on the local structure of the data center, such as equipment locations and server types. Although the various physical relations in a data center were modeled as a network in our previous work, there were no mechanisms for automatically extracting the structure of the data center. The use of a CNN is a technique for learning local structure adaptively, which allows learning complicated features, such as the various physical relations in a data center. We evaluate the performance of the proposed model by using actual data from an experimental data center. The evaluation indicates that the proposed model can predict 20-minute future temperature distributions over 48 locations in 0.42 ms, with a root mean square error (RMSE) of 0.96 degrees. This accuracy is a dramatic improvement over simple linear prediction models, and the accuracy is sufficient to allow for control of air conditioners on the basis of these temperature predictions.

To reduce the power consumption in data centers, the coordinated control of the air conditioner and the serversis required. It takes tens of minutes for changes of operationalparameters of air conditioners including outlet air temperatureand volume to be reflected in the temperature distribution inthe whole data center. So, the proactive control of the airconditioners is required according to the prediction temperaturedistribution corresponding to the load on the servers. In thispaper, the temperature distribution and the power efficiencyof air conditioner were predicted by using a machine-learningtechnique, and also we propose a method to follow-up proactivecontrol of the air conditioner under the predicted optimumcondition. Consequently, by the follow-up proactive control ofthe air conditioner and the load of servers, power consumptionreduction of 30% at maximum was demonstrated.

Locomotion on the rough and variable ground surfaces is crucial for robots to complete various tasks. Recent advancement in numerical computation allow such locomotive robots to manipulate in real environments using a model-based control framework. This approach is successful if the precise model it is obtained. However, it is not always feasible to use the precise model because there are various factors related to both the robot and its surroundings. In this paper, we report the locomotion experiments with 2 types of legged robot that can change its behavior by switching connection patterns among hydraulic cylinders mounted on the robot's legs. An actuator network system (ANS) is used to switch mutual interconnection of cylinders. The switching connection allows the robots to realize not only adaptive locomotion to the environment but also operation of the traveling direction.

To decrease the power consumption of data centers, coordinated control of air conditioners and task assignment on servers is crucial. It takes tens of minutes for changes of operational parameters of air conditioners including outlet air temperature and volume to be actually reflected in the temperature distribution in the whole data center. Proactive control of the air conditioners is therefore required according to the predicted temperature distribution, which is highly dependent on the task assignment on the servers. In this paper, we apply a machine learning technique for predicting the temperature distribution in a data center. The temperature predictor employs regression models for describing the temperature distribution as it is predicted to be several minutes in the future, with the model parameters trained using operational data monitored at the target data center. We evaluated the performance of the temperature predictor for an experimental data center, in terms of the accuracy of the regression models and the calculation times for training and prediction. The temperature distribution was predicted with an accuracy of 0.095°C. The calculation times for training and prediction were around 1,000 seconds and 10 seconds, respectively. Furthermore, the power consumption of air conditioners was decreased by roughly 30% through proactive control based on the predicting temperature distribution.

To achieve a realistic task by a recent complicated robot, a practical motion planning method is important. Especially in this decade, sampling-based motion planning methods have become popular thanks to recent high performance computers. In sampling-based motion planning, a graph that covers the state space is constructed based on reachability between node pairs, and the motion is planned using the graph. However, it requires an explicit model of a controlled target. In this research, we propose a motion planning method in which a system model is estimated by using Gaussian process regression. We apply our method to the control of an actual robot. Experimental results show that the control of the robot can be achieved by the proposed motion planning method.

The compliance of muscles with external forces and the structural stability given by biarticular muscles are important features of animals to realize dynamic whole body motions such as running and hopping in various environments. For this reason, we have been studying an electromagnetic linear actuator. This actuator can emulate the behavior of a human muscle such as the spring-damper characteristics by quick control of the output force (i.e. impedance control) and it is expected to be used as an artificial muscle. In this paper, we develop a monopedal robot possessing bi- and mono-articular muscles implemented by linear actuators. Thanks to the biarticular muscle, the bouncing direction of the robot can be controlled by changing the stiffness ellipse at the endpoint (i.e. foot) of the robot. We confirm that the bouncing direction of the robot can be controlled and hopping can be achieved by changing the stiffness ellipse.

This paper deals with a multiagent path-planning problem where several robots track humans to obtain detailed information on human behaviors and characteristics. For this, agents' paths are planned on the basis of the similarity between the predicted positions of humans and the agents' field of view. The long-horizon path planned on the basis of an accurate long-horizon prediction improves the tracking performance. However, it requires heavy computation and is less useful if the prediction is inaccurate. Since the accuracy of the prediction depends on the situation, the prediction term is determined by the similarity between the current and previous predictions. The results of computer simulation showed that our path-planning method works well for trajectories of humans in a dynamic environment by changing the horizon length of the path planning.

This paper deals with a multi-agent path planning problem for tracking humans in order to obtain detail information such like human behavior and characteristics. To achieve this, paths of agents are planned based on similarity between the predicted positions of humans and agents' field of views, and the path length in the path planning is determined according to the consistency between the current prediction and the previous prediction of the future human positions. We conducted computer simulations and results showed that our path planning method works well for trajectories of human in a real environment.

Biological creatures, some of them are very simple, seem to perform efficient search strategy. Recent researches show that noise in their internal mechanism may have an important role to manage this behavior. This paper focuses on realizing a simple, noise utilizing, mathematical framework that enables a mobile robot to perform random search adaptively and efficiently under changing target density. Our approach is to model and implement bacterial movement based on a recent perspective of noise utilizing mechanism in living beings: biological fluctuation. As a result, the robot will adaptively switch its random search pattern between Levy walk and Brownian walk to increase the search efficiency in a patchy environment where the target density naturally alternates.

Multi-hop wireless networks (MWNs) are rapidly gaining attention, because they can provide a wide coverage area to Internet users. To provide stable and high-performance network environments, security issues must be addressed. This paper focuses on the security issues in terms of anomalous relay nodes inside networks because their malicious behaviors can degrade the performance of MWNs. To maintain the network performance of MWNs, we propose a novel network reconfiguration scheme that each node reconstructs a network autonomously using the I/F of the neighbor nodes linked to a misbehaving node. Our proposed scheme reconfigurates topology with an emphasis on the reuse of I/F, the number of the links required to construct, transmission rates, performance anomaly, and network connectivity. We evaluate the effectiveness of the proposed schemes by simulations. The results of simulation indicate that the proposed scheme can prevent the communication performance degradation of the entire MWN.

Many biologically inspired approaches have been investigated in relation with researches on mobile robot(s) that can effectively locate targets that induce gradient information. Here, we concentrate on realizing an adaptive searching behavior in mobile robot that is simple, yet effective with and without sensing the gradient information. We are interested in two searching behaviors found in biological creatures: bacterial chemotaxis, probably the simplest yet effective gradient sources searching behavior found in living creatures; and Levy walk, specialized random walks with fractal movement trajectories that optimize random search for sparsely and randomly distributed target(s). Our approach is to implement and combine the two searching behaviors based on ¿yuragi¿, a Japanese word for biological fluctuation.

Living organisms have various kinds of flexibility and robustness which are realized by ¿yuragi,¿ i.e. biological fluctuations or noises. Bacterial motion is a form of noise-based motion, since bacteria can move towards a higher concentration of some chemical which they prefer even though they have only a limited 1-DOF of flagella for mobility. Bacteria also have only a limited sensory device which cannot detect the spatial gradient of the chemical at a time. The simple strategies that bacteria take to realize chemotaxis are (1) to tumble (or turn) to change orientation randomly with unbundled flagella in various directions being hit by surrounding water molecules, and (2) to change the frequency of tumbling according as the time change of chemical concentration. In this paper, we describe a small and simple, 1-DOF swimming robot developed by mimicking the bacterial motion generation mechanism. The robot only has a single motor and a single sensor (a photo detector), however, by changing its orientation in response to various noises which exist in the environment, and by changing the frequency of turning, the robot can approach its goal. Experimental results indicate that the robot statistically approaches the goal (a light source) in two dimensional space with its 1-DOF actuator, which would be impossible for the robot to achieve without utilizing noises in the environment.

Recently, many humanoid robots have been developed and actively investigated all over the world in order to realize partner robots which can coexist in an environment shared with humans. having good communication skills is essential in order to interact naturally with humans. However, even with state-of-the-art interaction technology, it is still difficult for humans to interact with humanoid robots without conscious effort. In this paper, we use the android robot, which has an appearance which is quite similar to that of a human, as a bystander in human-human communication. The android is not explicitly involved in the conversation, however it makes small reactions to the behavior of the humans, and the psychological effects of its behavior on the human subjects are investigated. Through the experiments, it is shown that if the android mimics the behavior of the subject this can be quite effective in harmonizing the human-human communication.

Recently, many researchers on humanoid robotics are interested in quasi-passive-dynamic walking (quasi-PDW) which is similar to human walking. It is desirable that control parameters in quasi-PDW are automatically adjusted because robots often suffer from changes in their physical parameters and the surrounding environment. Reinforcement learning (RL) can be a key technology to this adaptability, and it has been shown that RL realizes quasi-PDW in a simulation study. To apply the existing method to controlling real robots, however, requires further improvement to accelerate its learning, otherwise the robots will break down before acquiring appropriate controls. To accelerate the learning, this study employs off-policy natural actor-critic (off-NAC), and applies it to an acquisition problem of quasi-PDW. The most important feature of the off-NAC is that it reuses the samples that has already been obtained by previous controllers. This study also shows an adaptive method of the learning rate. Simulation as well as real experiments demonstrate that fast and stable learning of quasi-PDW of an unstable biped robot can be realized by our modified off-NAC

A class of biped locomotion called passive dynamic walking (PDW) has been recognized to be efficient in energy consumption and a key to understand human walking. Although PDW is sensitive to the initial condition and disturbances, some studies of quasi-PDW, which introduces supplementary actuators, are reported to overcome the sensitivity. In this article, for realization of the quasi-PDW, an on-line learning scheme of a feedback controller based on a policy gradient reinforcement learning method is proposed. Computer simulations show that the parameter in a quasi-PDW controller is automatically tuned by our method utilizing the passivity of the robot dynamics. The obtained controller is robust against variations in the slope gradient to some extent.