As entertainment robots gain popularity, the demand for natural and expressive motion, particularly in dancing, continues to rise. Traditionally, dancing motions have been manually designed by artists, a process that is both labor-intensive and restricted to simple motion playback, lacking the flexibility to incorporate additional tasks such as locomotion or gaze control during dancing. To overcome these challenges, we introduce Deep Fourier Mimic (DFM), a novel method that combines advanced motion representation with Reinforcement Learning (RL) to enable smooth transitions between motions while concurrently managing auxiliary tasks during dance sequences. While previous frequency domain based motion representations have successfully encoded dance motions into latent parameters, they often impose overly rigid periodic assumptions at the local level, resulting in reduced tracking accuracy and motion expressiveness, which is a critical aspect for entertainment robots. By relaxing these locally periodic constraints, our approach not only enhances tracking precision but also facilitates smooth transitions between different motions. Furthermore, the learned RL policy that supports simultaneous base activities, such as locomotion and gaze control, allows entertainment robots to engage more dynamically and interactively with users rather than merely replaying static, predesigned dance routines.

As home robotics gains traction, robots are increasingly integrated into households, offering companionship and assistance. Quadruped robots, particularly those resembling dogs, have emerged as popular alternatives for traditional pets. However, user feedback highlights concerns about the noise these robots generate during walking at home, particularly the loud footstep sound. To address this issue, we propose a sim-to-real based reinforcement learning (RL) approach to minimize the foot contact velocity highly related to the footstep sound. Our framework incorporates three key elements: learning varying PD gains to actively dampen and stiffen each joint, utilizing foot contact sensors, and employing curriculum learning to gradually enforce penalties on foot contact velocity. Experiments demonstrate that our learned policy achieves superior quietness compared to a RL baseline and the carefully handcrafted Sony commercial controllers. Furthermore, the trade-off between robustness and quietness is shown. This research contributes to developing quieter and more user-friendly robotic companions in home environments.

Processes on virtual machines can migrate from a host server to a guest server by migrating the virtual machines. In this paper, we propose a DMMV (Dynamic Migration of Multiple Virtual machines) algorithm where virtual machines migrate from a host server to a more energy-efficient guest server. Here, virtual machines are dynamically suspended and resumed depending on the number of processes. In addition, one or more than one virtual machine is selected to migrate from a host server to a guest server. The number of virtual machines to migrate is decided so as to minimize the electric energy consumption of the host and guest servers. In the evaluation, we show not only the total electric energy consumption and active time of servers but also the average execution time of processes can be reduced in the DMMV algorithm compared with other algorithms.

It is critical to reduce the electric energy consumption of servers in a cluster. In this paper, we discuss a migration approach to reducing the electric energy consumption of servers where a virtual machine with application processes migrates to a more energy-efficient server. In this paper, we consider a homogeneous cluster. We newly propose an ISEAM2H algorithm. Here, a virtual machine on a host server is selected to perform a process issued by an application and a guest server to which a virtual machine migrates is found so that the total electric energy consumption of servers can be minimized. In the evaluation, we show the total electric energy consumption and active time of the servers and the average execution time of processes can be reduced in the ISEAM2H algorithm.

The Internet of Things (IoT) has been applied to home/office, healthcare, intelligent transportation and agriculture systems. The new IoT technology are growing rapidly and will play an essential role in our future societal lifestyle, economy and business. Currently, power hungry and radio wave interference are two big challenges hindering the IoT development. In this study, we propose a Markov localization algorithm to estimate positions of IoT devices considering various gateway allocation scenarios in a widespread and boundaryless field. We adopt the model-checking technique to validate the convergence of positions of the IoT devices. Our approach can accurately identify positions of IoT devices and connect each IoT node to its nearest gateways for sending sensing data and receiving commands from the cloud computing resources. We use a cattle-breeding IoT network as a case study to validate the proposed approach,which reduces IoT power consumption while enhancing the connectivity of the network. Additionally, we also discuss how to apply this simple approach to real-world IoT networks such as smart home and vehicular ad-hoc networks.

Summary In order to realize green society, it is critical to reduce electric energy consumed by servers in clusters. In our previous studies, these types of algorithms are proposed to select an energy‐efficient server to perform an application process issued by a client. In this paper, we newly discuss a migration approach to reducing the electric energy consumption of servers where virtual machines with application processes migrate to more energy‐efficient servers. We propose a new algorithm called ISEAM2 to reduce the electric energy consumption of servers. Here, a pair of a host server and a virtual machine on the host server are first selected to perform a process issued by an application. In addition, a target virtual machine on a host server and a guest server to which the target virtual machine migrates are selected so that the electric energy consumption of the host and guest servers can be minimized. Thus, virtual machines with application processes migrate to more energy‐efficient servers. We also propose a simple way to estimate the termination time of every process on each server. In the evaluation, we show the total electric energy consumption and total active time of servers, and the average execution time of processes can be reduced in the ISEAM2 algorithm compared with other algorithms.

Virtual machines are now widely used to support applications with virtual computation service in server clusters. Here, a virtual machine can migrate to a guest server while processes are being performed. In this paper, we discuss a virtual machine migration approach to reducing the electric energy consumption of servers. We newly propose two types of algorithms, energy-aware virtual machine selection (EVMS) algorithm to select a virtual machine to perform a process newly issued by a client and energy-aware virtual machine migration (EVMM) algorithm to migrate a virtual machine to a guest server. Here, the termination time of each virtual machine is estimated without considering each process. We evaluate the EVMS and EVMM algorithms and show the total electric energy consumption and active time of servers and the average execution time of processes can be reduced compared with other non-migration algorithms.

In clusters of servers like cloud computing systems, computation resources like CPUs and storages are virtualized. Virtual machines are now widely used to support applications with virtual computation service on computation resources. Application processes are performed on virtual machines independently of which servers of which architectures are used. Furthermore, a virtual machine can migrate from a host server to a guest server while processes are being performed on the virtual machines. In this paper, we take advantage of the migration technologies of virtual machines to reduce the electric energy consumed by servers. We propose a modified simple virtual machine migration (MSVM) algorithm to migrate a virtual machine to another energy-efficient server in order to reduce the electric energy consumption. Here, the amount of computation to be performed by processes on a virtual machine is estimated only by using the number of the processes. We show the total electric energy consumption of the servers can be reduced in the MSVM algorithm compared with non-migration algorithms.

We have to reduce the electric energy consumed by servers in a cluster. In our previous studies, types of algorithms to select an energy-efficient server for a request process are proposed. Furthermore, schemes for energy-efficiently migrating a process and replicas of a process are discussed where a process and a replica migrate on a host server to a more energy-efficient guest server in our previous studies. However, it is not easy to realize the migration of processes on types of servers. Virtual machine (VM) technologies are now widely used to support applications with virtual resource service in could computing systems. Here, a virtual machine with application processes can migrate from a host server to another guest server. In this paper, we newly propose an energy-aware migration scheme of virtual machines (EAMV). Here, processes on a virtual machine can migrate to a server which consumes smaller electric energy and can be energy-efficiently performed in a cluster. We implement the EAMV scheme and evaluate the migration scheme in terms of energy consumption of servers and execution time of each process.

We have to reduce the electric energy consumed by servers in a cluster in order to realize eco-society. Types of algorithms for a request process to select an energy-efficient server in a cluster of servers are proposed in our previous studies. Furthermore, algorithms for energy-efficiently migrating a process on a host server to a more energy-efficient guest server is discussed. Virtual machines are now widely used to support applications with virtual computation service in cloud computing systems. Here, a virtual machine can migrate to a guest server, e.g. which is less loaded. By migrating a virtual machine, application processes performed on the virtual machine can also migrate from a host server to another guest server. In this paper, we newly propose an energy-aware migration algorithm of virtual machines (EAMV). Here, processes on a virtual machine can migrate to a guest server which consumes smaller electric energy and can be energy-efficiently performed in a cluster. We evaluate the EAMV algorithm compared with non-migration algorithms in terms of the total electric energy consumption and execution time of processes. We show the electric energy consumption and average execution time can be reduced in the EAMV algorithm.

The present paper focuses on applications that are periodic and have both latency and throughput constraints. For these applications, pipeline scheduling is effective for reducing energy consumption. Thus, the present paper proposes a pipelined task scheduling method for minimizing the energy consumption of GALS MP-SoC under latency and throughput constraints. First, we model target GALS MP-SoC architecture and application tasks. We then show that the energy optimization problem under this model belongs to the class of mixed-integer linear programming. Next, we propose a new scheduling method based on simulated annealing for the purpose of solving this problem quickly. Finally, experimental results demonstrate that the proposed method achieves a significant energy reduction on a real application under a practical architecture

This paper presents a novel power reduction method for chip multi-processors (CMPs) under real-time constraints. While the power consumption of processing units (PUs) on CMPs can be reduced without violating real-time constraints by dynamic voltage and frequency scaling (DVFS), the clock frequency of each PU cannot be determined independently because of the performance impact caused by the conflict for the shared resources. To minimize power consumption in this situation, we first derive an analytical model which provides the optimal priority and clock frequency setting, and then propose a method of controlling the priority of shared resource accesses in cooperation with DVFS. From the analytical model, in dual-core CMPs, we reveal that the total power consumption is minimized when the clock frequency of two PUs becomes the same. An experiment with a synthetic benchmark supports the validity of the analytical model and the evaluation results with real applications show that the proposed method reduces the power consumption by up to 15% and 6.7% on average compared with a conventional DVFS technique.