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In this paper, we address sequential mobility assistance for daily elderly care through physical human–robot interaction. The goal of this work is to develop a robotic assistive system to provide p...

Although numerous swarm robotic systems have already been developed, they have exhibited limited adaptability. This was partly because the previous researchers designed the control schemes on the basis of informational interaction, without considering the physical effects of the environment. To tackle this problem, we employ an unconventional approach: we design a control scheme for swarm robots based on their physical interaction in a hydrodynamic field, inspired by biological swarming. Through simulations using a smoothed particle hydrodynamics method, we show that the proposed control scheme allows agents to form an ordered swarm in response to their environment.

査読付原著論文, インパクトファクター(0.57), 被引用回数(0)

This study is intended to deal with the interplay between control and mechanical systems, and to discuss the 'brain–body interaction as it should be', particularly from the viewpoint of learning. To this end, we have employed a decentralized control of a two-dimensional serpentine robot consisting of several identical body segments as a practical example. The preliminary simulation results derived indicate that the convergence of decentralized learning of locomotion control can be significantly improved, even with an extremely simple learning algorithm, i.e., a gradient method, by introducing biarticular muscles which induce long-distant physical interaction between the body segments compared to the one only with monoarticular muscles. This strongly suggests the fact that a certain amount of computation should be offloaded from the brain into its body, which allows robots to emerge various with interesting functionalities.

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization.