Abstract Peristaltic pumps are widely employed in fields such as medicine, food industries, and biotechnology. In nature, it is known that gastropods such as snails, which utilize peristaltic motion for locomotion, generate propulsion in the direction opposite to the propagation of their muscle contraction waves. Therefore, in this study, we developed a transport device capable of bidirectional transport within a single channel by employing a mechanism that mimics this gastropod peristalsis. The device is composed of a parallel link mechanism and a flexible silicone rubber tube, inducing continuous peristaltic motion in the tube solely through motor rotation. By utilizing eccentric shafts to generate waveform motion and continuously converting the force transmitted to the tube section, smooth wave-like motion was achieved. Experimental results demonstrated that while liquid is transported in the direction of wave propagation, slender objects constrained by the tube walls are transported in the opposite direction. This paper presents the proposal and analysis of this transport mechanism and aims to provide new insights into conventional fluid and object transport technologies.

Abstract To enable robots to coexist with humans in dynamic real-world environments, they must possess both autonomy and versatility. Such autonomy requires the ability to interpret situations and select appropriate actions, which goes beyond simple image recognition. Humans rely on common sense and “affordances”—perceived action possibilities in a given context—to determine behavior. For example, the presence of an apple on a plate may lead to different reactions depending on its placement. While recent advancements in object detection models like YOLO have enabled AI to recognize and relate objects, these systems lack affordance information and thus struggle with situational understanding. Affordances are a form of implicit knowledge closely tied to human common sense, making them difficult for conventional AI to process. However, large language models (LLMs) such as ChatGPT, which have been trained on vast human-generated texts, may possess a form of embedded common sense. Building on this, we propose a method for automatically acquiring affordances from symbols using LLM outputs. Our method involves three steps: generating descriptive text via LLMs, analyzing it through morphological and dependency parsing to reconstruct a symbol network, and then calculating affordances based on the network’s structure. An initial experiment using the object “apple” demonstrated the ability to extract scene-specific affordances with high explainability. Ultimately, this approach enables affordance re-cognition in a manner similar to human reasoning.

Abstract Many-legged arthropods, such as myriapods, exhibit coordinated stepping patterns during walking. Although they are known to use similar inter-limb coordination patterns across species, it remains elusive whether common control principles exist for many-legged locomotion. To address this issue, we observed millipede walking and found that subtle trunk-pitch bending, coordinated with leg movements, sometimes appears. Drawing inspiration from this finding, we hypothesized that inter-limb coordination in many-legged locomotion could be generated via trunk–limb coordination control. To test this hypothesis, we constructed a neuro-mechanical model of many-legged animals and proposed a simple trunk–limb coordination control based on local sensory feedback. In simulations, we verified that our model can reproduce the inter-limb coordination of many-legged arthropods with various leg numbers. We expect that our model will provide new insights into the control principles underlying many-legged locomotion.

Abstract We address the challenge of coordinating multiple robots in narrow and confined environments, where congestion and interference often hinder collective task performance. Drawing inspiration from insect colonies, which achieve robust coordination through stigmergy—modifying and interpreting environmental traces—we propose a stigmergic multi-agent deep reinforcement learning (S-MADRL) framework that leverages virtual pheromones to model local and social interactions, enabling decentralized emergent coordination without explicit communication. To overcome the convergence and scalability limitations of existing algorithms such as MADQN, MADDPG, and MAPPO, we leverage curriculum learning, which decomposes complex tasks into progressively harder sub-problems. Simulation results show that our framework achieves the most effective coordination of up to eight agents, where robots self-organize into asymmetric workload distributions that reduce congestion and modulate group performance. This emergent behavior, analogous to strategies observed in nature, demonstrates a scalable solution for decentralized multi-agent coordination under crowded environments with communication constraints.

Abstract Nailfold capillaries, which are located at the base of the fingernail, provide a non-invasive window into peripheral microcirculation and are valuable for the early detection of systemic diseases such as diabetes mellitus and scleroderma, with abnormalities in capillary morphology, such as twisting and enlargement, indicative of vascular damage due to factors such as glucose accumulation. Traditional imaging modalities such as ultrasound and laser imaging have been used to evaluate peripheral blood vessels; however, such methods are often expensive and burdensome. Recently, the use of machine learning with microscopic imaging has shown promise in classifying capillary structures. However, these methods typically require large, annotated datasets, limiting the practicality; thus, we propose a novel approach that uses transfer learning to classify the shape of nailfold capillaries, specifically the presence or absence of capillary twisting. A pre-trained model based on fundus vessel images was fine-tuned for this task, leveraging the structural similarity between retinal and nailfold capillaries. The proposed method was found to achieve accurate and efficient classification even with a limited nailfold dataset, demonstrating its potential for practical clinical use.

Abstract Reservoir computing (RC) is a computer architecture allowing us to utilize any nonlinear dynamical system as a computing device called a reservoir. Exploiting elementary cellular automata (ECAs) as reservoirs is no exception. Recent studies have shown that the RC implemented by asynchronously tuned ECAs (AT_ECAs) has an advantage of enhancing the learning ability to identify multiple patterns, suggesting effects of critical spacetime patterns that the AT_ECAs create in a wide range of transition rules. However, the generalization capability of the AT_ECA-based RC remains unclear. This study evaluated the generalization performance of the AT_ECA-based RC using the temporal parity task in comparison with the ECA-based RC. We found that the AT_ECA-based RC demonstrated higher performance than ECA-based RC in most rules. This might have been a result of critical behaviors that the AT_ECAs universally generate with different mechanisms from the ECAs.