Controlling swarm dynamics is challenging and has long been an attractive research field because swarms provide a fundamental insight of locally interacting systems’ emergent behaviors. For example, a sheepdog type navigation control has been studied recently, where swarms consist of two different agents: passive sheep and active sheepdogs. In this paper, we focused on the swarm predator system with a swarm that has a number of passive agents and a single active predator agent. Recently, reservoir computing (RC) was introduced as a new way to control swarms. RC offers an easy and analyzable way to find optimal controllers. In this paper, we suggest a new way to read the swarms’ state for controlling swarm predator systems, named relatively ordered state (ROS), where the agents’ IDs are reordered at each time step by relative distances from the predator. The ROS is robust against the swarm’s initial condition’s difference, despite the simpleness and naturalness of the process of the ROS. We found that a swarm within the critical phases of order disorder phase transition like structures can bring out the swarm’s potential to be a reservoir both in open-loop and closed-loop experiments. In this closed-loop control, the predator determines its own movement via the collective dynamics of the swarm like a serpent eating its own tail in the classic “Ouroboros.”

The softness of robots that navigate the real world is frequently hamstrung by rigid elements such as traditional computers. One avenue that may reduce the reliance on these types of devices is the application of physical reservoir computing (PRC). Previous studies have shown that by leveraging the under-actuated nonlinear dynamics of soft mechanisms, complex computing tasks can be achieved. In this study we present a new octopus-inspired walking and swimming robot: Takorobo. We investigated the degree to which locomotion significant tasks (including body motion prediction and direct actuator control) can be embedded into this robot using its four soft sensory tentacles. The robot was found to be able to accurately compute its body motions and successfully exercise direct closed-loop PRC control on both land and water for some control signals.

In recent years, swimming robots have been developed to achieve efficient propulsion and high maneuverability that are possessed naturally by fish. Previous studies have attempted to achieve swimming similar to fish by control based on physical models and top-down architectures, but have encountered problems due to the high complexity of the underwater environment. Several research works have tried to overcome these problems by exploiting embodiment—that is, by mimicking the physical properties of fish. To achieve more intelligent swimming from the perspective of the embodiment, we focused on a framework called physical reservoir computing (PRC). This framework allows us to utilize physical dynamics as a computational resource. In this study, we propose a soft sheet-like swimming robot and a PRC-based architecture that can be used to emulate swimming motions by exploiting its own body dynamics for closed-loop control. Through experiments, we demonstrated that our system satisfies the properties required for learning swimming motion through supervised learning. We also succeeded in robust motion generation and environmental state estimation, opening up future prospects for more intelligent robot control and sensing.

How does a single spiking neuron process information? This question is a long lasting one, which has been constantly posed and pursued by many researchers from different perspectives. In this paper, we tackle this issue from the perspective of reservoir computing using a single Izhikevich neuron as a model system. To prepare reservoir nodes from the response of a single Izhikevich neuron, we used of a technique called time multiplexing, which exploits a time-scale difference between input-output series and the transient dynamics of the single neuron. Based on this scheme, we evaluated the information processing capability of a single Izhikevich neuron using a standard benchmark task. Furthermore, we measured its memory capacity and showed its characteristic memory profile in various parameter settings. Finally, the relationships between the dynamical properties of the Izhikevich neuron and its memory capacity are discussed in detail.