In this paper, we employ statistics of local governments (city, town, or village) in synthesizing individual households of a prefecture to reduce the error between synthesized statistics and actual statistics. Our previous method used estimated statistics of local governments with fewer than 200,000 people since those smaller local governments do not release their detailed statistics. To reduce the errors with prefectural statistics, we simultaneously employ statistics of smaller local governments in synthesizing population in a prefecture. Our experimental results show that we can make a 1/7 to 1/140 reduction in the error between synthesized statistics and actual statistics compared to the previous method.

Housekeeping robot must grow as it lives with the user. To build a system that can serve as a foundation for this purpose, we need to discover what we need to research further through the realization of a housework support task by a robot, and need to solve these problems. In this paper, we tackle the task of bento-serving as a household chore support task, and describe new problems that we discovered through the implementation of it. As for the bento-serving, it has been assumed that the robot performs the task in a factory. In this paper, we reconsider the bento-serving task by a robot as a part of our household support and propose a system to make it happen. When a housework assistance robot does the bento-serving, he needs to recognize the prepared foods and environment, interact with the owner, and manipulate foods without destroying them. Through the realization of the bento-serving task, we found that the housekeeping robot has to deal with objects of the same type but with different shapes, plan movements with user preferences in mind, and perform the entire sequence of actions by himself. To achieve these goals, we propose a system consisting of five processes, recognition of the environment and food, acquisition of information through dialogue, storage of the information, devising a goal using the acquired information, and manipulation of the objects using tools.

This paper proposes a novel walk control for hexapod robots based on Follow-the-Contact-Point (FCP) gait control, which can reduce or increase the number of walking legs during walking. The FCP gait control is decentralized event-driven gait control for multi-legged robots. In order to adapt the FCP gait control to changing the number of walking legs, we first introduce locomotion flag for each leg and adjacent information between legs. Then, to implement the adjacent information, the transition conditions of the control algorithm of FCP gait control are improved. As a result, the control algorithm obtains the properties of deadlock-free and maintaining the support triangle. The effectiveness of the proposed control architecture is verified by experiments in which a hexapod robot changes the number of walking legs during walking and walks on rough terrain even when the number of walking legs is reduced.

This work discusses two data-driven control methods: Virtual Reference Feedback Tuning (VRFT) and Data-Driven Closed-Loop Update Tuning (DD-CLUT). Although they are derived from different discussions, it is shown that DD-CLUT is a special case of VRFT. In particular, DD-CLUT is shown to be equivalent to the suboptimal VRFT. This result implies that suboptimal VRFT tunes the controller parameter so as to minimizes a weighted error between the updated closed-loop system and reference model.