Snakes change their locomotion patterns in response to the environment. This ability is a motivation for developing snake-like robots with highly adaptive functionality. In this study, a decentralised control scheme of snake-like robots that exhibited autonomous gait transition (i.e. the transition between concertina locomotion in narrow aisles and scaffold-based locomotion on unstructured terrains) was developed. Additionally, the control scheme was validated via simulations. A key insight revealed is that these locomotion patterns were not preprogrammed but emerged by exploiting Tegotae, a concept that describes the extent to which a perceived reaction matches a generated action. Unlike local reflexive mechanisms proposed previously, the Tegotae-based feedback mechanism enabled the robot to 'selectively' exploit environments beneficial for propulsion, and generated reasonable locomotion patterns. It is expected that the results of this study can form the basis to design robots that can work under unpredictable and unstructured environments.

Irruptions of large herbivores, with a rapid population increase to peak abundance, are widely observed. Occasionally, there is a population crash following such peaks in abundance, after which the population recovers to form another peak typically lower than the initial. There are mathematical models describing this full cycle of irruptive dynamics. The insight for further improvement of such mathematical models will be obtained from demographic analyses of irruptive dynamics incorporating density‐dependent and density‐independent resource limitations. Using a 35‐yr dataset on the irruptive dynamics of an introduced sika deer (Cervus nippon) population on Nakanoshima Island, Japan, we evaluated the factors and stages determining irruptive dynamics (phase 1: the initial irruption and population crash; phase 2: subsequent dynamics) through key‐factor/key‐stage analysis of the population structure, estimated using age‐at‐death data of naturally dead deer. We set two factors (i.e., deer density and snow accumulation) and three life stages (i.e., immature individuals, adult female, and adult male). The estimated population structure showed two population crashes and a density peak higher than the initial peak during population regrowth subsequent to the initial population crash. The most influential factor and stage for determining the population dynamics differed between phases 1 and 2. The contribution of deer density to the variability in population change was the largest in phase 1 (62.30%) and decreased to 24.10% in phase 2. The contribution of snow accumulation was small in both phase 1 (11.74%) and phase 2 (0.64%). The male stage had the largest contribution in phase 1 (39.23%), while the immature stage had the largest contribution in phase 2 (63.31%). The female stage had the smallest contribution in both phase 1 (24.19%) and phase 2 (17.67%). Population growth rate decreased, while carrying capacity increased, in phase 2 compared to phase 1. We suggest that the small contributions of density‐dependent and density‐independent resource limitations on population dynamics in phase 2 were related to the characteristics of alternative foods (fallen leaves and woody plants) that were newly utilized by deer in phase 2. We conclude that alternative resources potentially generate various irruptive dynamics, including unstable dynamics not expected in the classic paradigm.

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Educational support robots have been the focus of study in recent years. Studies have reported that robots providing educational support, based on cognitive apprenticeship theory, provided learners with effective collaborative learning. However, the robots were remote controlled, so no behavioral model was constructed of robots operating autonomously to provide educational support. Therefore, in this paper, we construct a behavioral model in which robots autonomously provide educational support based on cognitive apprenticeship theory. In addition, through a comparative experiment with a behavioral model providing educational support in accordance with learner requests, which is a conventional technique, we verify the learning effects of this behavioral model on university students.