In this paper, we introduce a framework for imitation learning in navigation that enables policy learning from one-shot images without a physical robot and facilitates the transfer of this policy from simulation to reality. Utilizing Neural Radiance Fields (NeRF), our approach generates a simulated environment and simultaneously models expert behavior. This removes the necessity for a physical robot during both the expert teaching phase and the agent’s learning process, allowing for the application of policies learned within the NeRF simulation to real-world robots. We validate our method by demonstrating the navigation with an actual robot using the policy learned by our approach. Moreover, we present a method for adapting to changes in the robot configuration, such as camera parameters and robot dimensions, by simulating adjustments in the robot configuration throughout the learning and assessing its generalizability.

Mobile robot navigation in crowded environments with pedestrians is a crucial challenge in realizing service robots that can assist people in their daily lives. Navigation methods for mobile robots in environments employing deep reinforcement learning have been extensively studied. However, addressing such unexpected situations is a significant challenge. This study presents an approach that discerns whether a situation has been supposed to utilize a normalizing flow and dynamically switches between learning- and rule-based methods. Specifically, the proposed method achieves a higher success rate than employing only a learning-based approach and reaches the destination faster than employing only a rule-based approach in unexpected situations. Experiments are conducted to validate the performance enhancement achieved with the proposed switching method in both simulated and real-world settings.

In recent years, labor accidents and a shortage of skilled workers due to an aging population have become significant issues at construction sites in Japan. To address these challenges, we are developing a Cyber-Physical System (CPS) platform called ROS2-TMS for Construction, which aims to improve both the efficiency and safety of earthwork operations. In this study, we propose a task management system for construction machinery using an open platform named OPERA as an additional function of ROS2-TMS for Construction. This task management system controls construction machinery using environmental information stored in a database, which collects and stores data from sensors deployed throughout the construction site, and an extended Behavior Tree. At the end of this study, the results of the initial validation tests of autonomous earthwork operations using an OPERA-compatible backhoe ZX200 are presented.

Detection of spatial change around a robot is indispensable in several robotic applications, such as search and rescue, security, and surveillance. The present paper proposes a fast spatial change detection technique for a mobile robot using an on-board RGB-D/stereo camera and a highly precise 3D map created by a 3D laser scanner. This technique first converts point clouds in a map and measured data to grid data (ND voxels) using normal distributions transform and classifies the ND voxels into three categories. The voxels in the map and the measured data are then compared according to the category and features of the ND voxels. Overlapping and voting techniques are also introduced in order to detect the spatial changes more robustly. We conducted experiments using a mobile robot equipped with real-time range sensors to confirm the performance of the proposed real-time localization and spatial change detection techniques in indoor and outdoor environments.

Scale conversion telexistence is an extended concept of telexistence. It comprises avatars of different scales compared with the human scale. Immersive telexistence experience requires the avatar to realize the operator's behavior instantaneously as soon as possible. It is important to match the avatar's behavior with the operator's behavior because the difference between the operator and the avatar tends to hurt the immersiveness. However, the scale difference between an operator with the avatar makes behavior mismatch and lets the operator experience under different physical constant world. For instance, gravity changes under scale conversion telexistence. Therefore, our previous work reveals its importance termed equivalent gravity condition for walking using a small biped robot. Thus, in this demonstration, we use a small biped robot as the different-scale avatar allow us to experience as if we are within the small robot. Attendees experience several steps walking under different gravity conditions.

This paper describes a sophisticated method to track irises in a monocular video sequence with a particle filter that uses a newly proposed Iris-Eyelid separability filter (IESF). In order to reduce the influence of eyelids and to cope with the variance of the appearance of eyes of different people, we propose an Iris-Eyelid separability filter (IESF), and use it to estimate the likelihood of hypotheses in the particle filter. We confirmed that our method has the ability of tracking various eyes for different people whose head motion including translation, rotation and zoom, even when people are wearing glasses. Through the comparative experiments of the IESF and the conventional circular separability filter (CCSF), we confirmed that our iris tracking algorithm is more robustly.

Real-time execution of applications is one of key requirements for Cyber-Physical Systems (CPS) that integrate computational and physical elements for our social infrastructure, such as robotics, transportation, and consumer appliances. In such real-time systems, a task must be executed so as not to violate given time constraints. Moreover, it is desirable that the execution time of the task is predictable precisely. When Out-of-Order (OoO) execution is adopted for real-time systems to enhance the performance, it is much difficult to predict execution time because of the feature of OoO execution. In order to deal with this problem, various schemes were proposed such as IPC control mechanism of Responsive Multithreaded (RMT) Processor. RMT Processor is a real-time microprocessor adopting simultaneous multithreading (SMT) architecture with OoO execution. Its IPC control mechanism which tries to adjust the number of instruction commits to meet a given target IPC. The IPC control scheme can be implemented not only on RMT Processor but also on various processors and can improve the predictability of execution time. However, if an error between target and actual IPCs is observed, it cannot cancel the error in the next control window, which is used in the control mechanism. Since such uncorrected errors are accumulated in the successive control window, the predictability of the execution time is degraded gradually. To overcome this problem, in this paper, we propose a thread speed control scheme for real-time microprocessors. This scheme is based on the IPC control mechanism on RMT Processor. Our proposed thread speed control scheme calculates an error between reference and actual IPCs, then it dynamically updates the reference IPC of the next control window in order to cancel the past errors. Our proposed scheme is designed and implemented on RMT Processor. The simulation results show that the error is reduced to 2.60 × 10-5% in case that four threads are executed simultaneously.