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Living organisms are far superior to state-of-the-art robots as they have evolved a wide number of capabilities that far encompass our most advanced technologies. The merging of biological and artificial world, both physically and cognitively, represents a new trend in robotics that provides promising prospects to revolutionize the paradigms of conventional bio-inspired design as well as biological research. In this review, a comprehensive definition of animal–robot interactive technologies is given. They can be at animal level, by augmenting physical or mental capabilities through an integrated technology, or at group level, in which real animals interact with robotic conspecifics. Furthermore, an overview of the current state of the art and the recent trends in this novel context is provided. Bio-hybrid organisms represent a promising research area allowing us to understand how a biological apparatus (e.g. muscular and/or neural) works, thanks to the interaction with the integrated technologies. Furthermore, by using artificial agents, it is possible to shed light on social behaviours characterizing mixed societies. The robots can be used to manipulate groups of living organisms to understand self-organization and the evolution of cooperative behaviour and communication.

Artificial Intelligence (AI) and automation are fostering more sustainable and effective solutions for a wide spectrum of agricultural problems. Pest management is a major challenge for crop production that can benefit from machine learning techniques to detect and monitor specific pests and diseases. Traditional monitoring is labor intensive, time demanding, and expensive, while machine learning paradigms may support cost-effective crop protection decisions. However, previous studies mainly relied on morphological images of stationary or immobilized animals. Other features related to living animals behaving in the environment (e.g., walking trajectories, different postures, etc.) have been overlooked so far. In this study, we developed a detection method based on convolutional neural network (CNN) that can accurately classify in real-time two tephritid species (Ceratitis capitata and Bactrocera oleae) free to move and change their posture. Results showed a successful automatic detection (i.e., precision rate about 93%) in real-time of C. capitata and B. oleae adults using a camera sensor at a fixed height. In addition, the similar shape and movement patterns of the two insects did not interfere with the network precision. The proposed method can be extended to other pest species, needing minimal data pre-processing and similar architecture.

Escape and surveillance responses to predators are lateralized in several vertebrate species. However, little is known on the laterality of escapes and predator surveillance in arthropods. In this study, we investigated the lateralization of escape and surveillance responses in young instars and adults of Locusta migratoria during biomimetic interactions with a robot-predator inspired to the Guinea fowl, Numida meleagris . Results showed individual-level lateralization in the jumping escape of locusts exposed to the robot-predator attack. The laterality of this response was higher in L. migratoria adults over young instars. Furthermore, population-level lateralization of predator surveillance was found testing both L. migratoria adults and young instars; locusts used the right compound eye to oversee the robot-predator. Right-biased individuals were more stationary over left-biased ones during surveillance of the robot-predator. Individual-level lateralization could avoid predictability during the jumping escape. Population-level lateralization may improve coordination in the swarm during specific group tasks such as predator surveillance. To the best of our knowledge, this is the first report of lateralized predator-prey interactions in insects. Our findings outline the possibility of using biomimetic robots to study predator-prey interaction, avoiding the use of real predators, thus achieving standardized experimental conditions to investigate complex and flexible behaviours.

In this study, a robotic fish inspired to carangiform swimmers has been developed. The artifact presents a new transmission system that employs the magnetic field interaction of permanent magnets to ensure waterproofness and prevention from any overload for the structure and the actuating motor. This mechanism converts the rotary motion of the motor into oscillatory motion. Such an oscillating system, along with the wire-driven mechanism of the tail, generates the required traveling wave in the robotic fish. The complete free swimming robotic fish, measuring 179 mm in length with a mass of only 77 g, was able to maintain correct posture and neutral buoyancy in water. Multiple experiments were conducted to test the robotic fish performance. It could swim with a maximal speed of 0.73 body lengths per second (0.13 m/s) at a tail beat frequency of 3.25 Hz and an electric power consumption of 0.67 W. Furthermore, the robotic fish touched the upper bound of the efficient swimming range, expressed by the dimensionless Strouhal number: 0.43 at 1.75 Hz tail beat frequency. The lowest energy to travel 1 meter was 4.73 Joules for the final prototype. Future works will focus on endowing the robot with energy and navigation autonomy, and on testing its potential for real-world applications such as environmental monitoring and animal–robot interaction.

Social immunity involves collective defensive strategies against infectious diseases. Despite its prevalence in eusocial insects, little is known about social immunity in non‐eusocial organisms like gregarious locusts. To address this gap, an emergent biohybrid approach bridging robotics and ethology is employed to study the behavior of the gregarious phase of Schistocerca gregaria in response to the entomopathogenic fungus Beauveria bassiana. Herein, the first animal–robot–microorganism interaction is developed to explore how infected biomimetic agents (IB) influence healthy locust behavior compared to healthy biomimetic agents (HB), as well as to infected and healthy non‐biomimetic controls (INB, HNB). Significant differences in locust responses to different agents, including latency duration, grooming behavior, tactile interactions, and aggression are observed. In healthy locusts, the increased grooming and tactile interactions in response to IB highlight potential preventive measures against pathogen transmission. Also, tactile interaction behavior is notably extended toward IB, emphasizing the role of reciprocal hygiene in limiting pathogens spread within the swarm. Infected locusts exhibit altered behaviors, including increased interaction with any robotic agents, potentially to be cleaned of fungal conidia. This animal–robot interaction study reveals social immunity dynamics in non‐eusocial organisms, with implications for pest control, evolutionary ecology, social complex systems, and bioinspired engineering design. This study explores social immunity in gregarious locusts using a groundbreaking animal–robot–pathogen interaction model. By comparing locust responses to biomimetic agents mimicking healthy and infected individuals, significant behavioral changes are revealed. Findings highlight increased grooming and tactile interactions as preventive measures against pathogen spread, offering insights into swarm behavior, pest control, and bioinspired robotics.

Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO 2 ). Understanding how soils respond to rising temperatures is critical for predicting carbon release and informing climate mitigation strategies. Q 10 , a measure of soil microbial respiration, quantifies the increase in CO 2 release caused by a Celsius rise in temperature, serving as a key indicator of this sensitivity. However, predicting Q 10 across diverse soil types remains a challenge, especially when considering the complex interactions between biochemical, microbiome, and environmental factors. In this study, we applied explainable artificial intelligence (XAI) to machine learning models to predict soil respiration sensitivity (Q 10 ) and uncover the key factors driving this process. Using SHAP (SHapley Additive exPlanations) values, we identified glucose-induced soil respiration and the proportion of bacteria positively associated with Q 10 as the most influential predictors. Our machine learning models achieved an accuracy of , precision of , an AUC-ROC of , and an AUC-PRC of , ensuring robust and reliable predictions. By leveraging t-SNE (t-distributed Stochastic Neighbor Embedding) and clustering techniques, we further segmented low Q 10 soils into distinct subgroups, identifying soils with a higher probability of transitioning to high Q 10 states. Our findings not only highlight the potential of XAI in making model predictions transparent and interpretable, but also provide actionable insights into managing soil carbon release in response to climate change. This research bridges the gap between AI-driven environmental modeling and practical applications in agriculture, offering new directions for targeted soil management and climate resilience strategies.

Jumping is a locomotion strategy widely evolved in both invertebrates and vertebrates. In addition to terrestrial animals, several aquatic animals are also able to jump in their specific environments. In this paper, the state of the art of jumping robots has been systematically analyzed, based on their biological model, including invertebrates (e.g., jumping spiders, locusts, fleas, crickets, cockroaches, froghoppers and leafhoppers), vertebrates (e.g., frogs, galagoes, kangaroos, humans, dogs), as well as aquatic animals (e.g., both invertebrates and vertebrates, such as crabs, water-striders, and dolphins). The strategies adopted by animals and robots to control the jump (e.g., take-off angle, take-off direction, take-off velocity and take-off stability), aerial righting, land buffering, and resetting are concluded and compared. Based on this, the developmental trends of bioinspired jumping robots are predicted.

Among territorial animals, several species are characterized by males showing the same initial behaviours towards both sexes, leading to significant chances of injuries against conspecifics. In this study, we investigated how visual stimuli exhibited by a female-mimicking robotic replica can be exploited by highly territorial Betta splendens males to discriminate males from females. In addition, we tested the effect of light stimuli, mimicking the colour pattern of a reproductive female, on the consistence of courtship displays in B. splendens males. The intensity of male behaviours used in both courtship and not-physical agonistic interactions (e.g. fin spreading and gill flaring) was not importantly modulated by different stimuli. Conversely, behavioural displays used specifically in male–female interactions significantly increased when the robotic replica colour pattern mimicked a reproductive female. Furthermore, male courtship behaviours obtained in response to the robotic replica exhibiting light stimuli were comparable with responses towards authentic conspecific females. Our biomimetic approach to establish animal–robot individual interaction can represent an advanced strategy for trait-based ecology investigation, a rapidly developing research field.

The use of robotics to establish social interactions between animals and robots, represents an elegant and innovative method to investigate animal behaviour. However, robots are still underused to investigate high complex and flexible behaviours, such as aggression. Here, Betta splendens was tested as model system to shed light on the effect of a robotic fish eliciting aggression. We evaluated how multiple signal systems, including a light stimulus, affect aggressive responses in B . splendens . Furthermore, we conducted experiments to estimate if aggressive responses were triggered by the biomimetic shape of fish replica, or whether any intruder object was effective as well. Male fishes showed longer and higher aggressive displays as puzzled stimuli from the fish replica increased. When the fish replica emitted its full sequence of cues, the intensity of aggression exceeded even that produced by real fish opponents. Fish replica shape was necessary for conspecific opponent perception, evoking significant aggressive responses. Overall, this study highlights that the efficacy of an artificial opponent eliciting aggressive behaviour in fish can be boosted by exposure to multiple signals. Optimizing the cue combination delivered by the robotic fish replica may be helpful to predict escalating levels of aggression.

Advances in DNA sequencing revolutionized plant genomics and significantly contributed to the study of genetic diversity. However, predicting phenotypes from genomic data remains a challenge, particularly in the context of plant breeding. Despite significant progress, accurately predicting phenotypes from high-dimensional genomic data remains a challenge, particularly in identifying the key genetic factors influencing these predictions. This study aims to bridge this gap by integrating explainable artificial intelligence (XAI) techniques with advanced machine learning models. This approach is intended to enhance both the predictive accuracy and interpretability of genotype-to-phenotype models, thereby improving their reliability and supporting more informed breeding decisions. This study compares several ML methods for genotype-to-phenotype prediction, using data available from an almond germplasm collection. After preprocessing and feature selection, regression models are employed to predict almond shelling fraction. Best predictions were obtained by the Random Forest method (correlation = 0.727 ± 0.020, an = 0.511 ± 0.025, and an RMSE = 7.746 ± 0.199). Notably, the application of the SHAP (SHapley Additive exPlanations) values algorithm to explain the results highlighted several genomic regions associated with the trait, including one, having the highest feature importance, located in a gene potentially involved in seed development. Employing explainable artificial intelligence algorithms enhances model interpretability, identifying genetic polymorphisms associated with the shelling percentage. These findings underscore XAI's efficacy in predicting phenotypic traits from genomic data, highlighting its significance in optimizing crop production for sustainable agriculture.