What does robotics offer animal behaviour?

Reactive shepherding along a dynamic path

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill employed in the herding of animals, crowd control, and evacuation operations. Integrating shepherding capabilities into robots holds promise to perform such tasks with increased efficiency and reduced labor costs. To date, robotic shepherds have only been designed to steer a herd towards a predetermined goal location without constraints on the trajectory. However, the tasks of a sheepdog encompass not only steering the herd but also (i) maintaining the herd within a designated area and (ii) averting dangers, obstacles, or undesirable terrain such as newly sown land. We present a decentralized control algorithm for multi-robot shepherding designed to guide a group of animals along a specified path delineated by two boundaries. The algorithm incorporates the additional objective of preserving the group within these boundaries. Simulation results reveal that, especially in sections of the path with sharp turns and a small distance between the boundaries, the group exhibits a tendency to deviate beyond the prescribed margin. Additionally, our findings emphasize the algorithm's sensitivity to the ratio of robot-group sizes and the magnitude of the group's velocity.

Translating Virtual Prey-Predator Interaction to Real-World Robotic Environments: Enabling Multimodal Sensing and Evolutionary Dynamics

Prey-predator interactions play a pivotal role in elucidating the evolution and adaptation of various organism's traits. Numerous approaches have been employed to study the dynamics of prey-predator interaction systems, with agent-based methodologies gaining popularity. However, existing agent-based models are limited in their ability to handle multi-modal interactions, which are believed to be crucial for understanding living organisms. Conversely, prevailing prey-predator integration studies often rely on mathematical models and computer simulations, neglecting real-world constraints and noise. These elusive attributes, challenging to model, can lead to emergent behaviors and embodied intelligence. To bridge these gaps, our study designs and implements a prey-predator interaction scenario that incorporates visual and olfactory sensory cues not only in computer simulations but also in a real multi-robot system. Observed emergent spatial-temporal dynamics demonstrate successful transitioning of investigating prey-predator interactions from virtual simulations to the tangible world. It highlights the potential of multi-robotics approaches for studying prey-predator interactions and lays the groundwork for future investigations involving multi-modal sensory processing while considering real-world constraints.

Bioinspired robots can foster nature conservation

We live in a time of unprecedented scientific and human progress while being increasingly aware of its negative impacts on our planet's health. Aerial, terrestrial, and aquatic ecosystems have significantly declined putting us on course to a sixth mass extinction event. Nonetheless, the advances made in science, engineering, and technology have given us the opportunity to reverse some of our ecosystem damage and preserve them through conservation efforts around the world. However, current conservation efforts are primarily human led with assistance from conventional robotic systems which limit their scope and effectiveness, along with negatively impacting the surroundings. In this perspective, we present the field of bioinspired robotics to develop versatile agents for future conservation efforts that can operate in the natural environment while minimizing the disturbance/impact to its inhabitants and the environment's natural state. We provide an operational and environmental framework that should be considered while developing bioinspired robots for conservation. These considerations go beyond addressing the challenges of human-led conservation efforts and leverage the advancements in the field of materials, intelligence, and energy harvesting, to make bioinspired robots move and sense like animals. In doing so, it makes bioinspired robots an attractive, non-invasive, sustainable, and effective conservation tool for exploration, data collection, intervention, and maintenance tasks. Finally, we discuss the development of bioinspired robots in the context of collaboration, practicality, and applicability that would ensure their further development and widespread use to protect and preserve our natural world.

The emergence of dynamic networks from many coupled polar oscillators: a paradigm for artificial life

This work concerns a many-body deterministic model that displays life-like properties such as emergence, complexity, self-organization, self-regulation, excitability and spontaneous compartmentalization. The model portraits the dynamics of an ensemble of locally coupled polar phase oscillators, moving in a two-dimensional space, that under certain conditions exhibit emergent superstructures. Those superstructures are self-organized dynamic networks, resulting from a synchronization process of many units, over length scales much greater than the interaction range. Such networks compartmentalize the two-dimensional space with no a priori constraints, due to the formation of porous transport walls, and represent a highly complex and novel non-linear behavior. The analysis is numerically carried out as a function of a control parameter showing distinct regimes: static pattern formation, dynamic excitable networks formation, intermittency and chaos. A statistical analysis is drawn to determine the control parameter ranges for the various behaviors to appear. The model and the results shown in this work are expected to contribute to the field of artificial life.

Motion perception based on ON/OFF channels: A survey

Motion perception is an essential ability for animals and artificially intelligent systems interacting effectively, safely with surrounding objects and environments. Biological visual systems, that have naturally evolved over hundreds-million years, are quite efficient and robust for motion perception, whereas artificial vision systems are far from such capability. This paper argues that the gap can be significantly reduced by formulation of ON/OFF channels in motion perception models encoding luminance increment (ON) and decrement (OFF) responses within receptive field, separately. Such signal-bifurcating structure has been found in neural systems of many animal species articulating early motion is split and processed in segregated pathways. However, the corresponding biological substrates, and the necessity for artificial vision systems have never been elucidated together, leaving concerns on uniqueness and advantages of ON/OFF channels upon building dynamic vision systems to address real world challenges. This paper highlights the importance of ON/OFF channels in motion perception through surveying current progress covering both neuroscience and computationally modelling works with applications. Compared to related literature, this paper for the first time provides insights into implementation of different selectivity to directional motion of looming, translating, and small-sized target movement based on ON/OFF channels in keeping with soundness and robustness of biological principles. Existing challenges and future trends of such bio-plausible computational structure for visual perception in connection with hotspots of machine learning, advanced vision sensors like event-driven camera finally are discussed.

A Systematic Literature Review of Decision-Making and Control Systems for Autonomous and Social Robots

In the last years, considerable research has been carried out to develop robots that can improve our quality of life during tedious and challenging tasks. In these contexts, robots operating without human supervision open many possibilities to assist people in their daily activities. When autonomous robots collaborate with humans, social skills are necessary for adequate communication and cooperation. Considering these facts, endowing autonomous and social robots with decision-making and control models is critical for appropriately fulfiling their initial goals. This manuscript presents a systematic review of the evolution of decision-making systems and control architectures for autonomous and social robots in the last three decades. These architectures have been incorporating new methods based on biologically inspired models and Machine Learning to enhance these systems’ possibilities to developed societies. The review explores the most novel advances in each application area, comparing their most essential features. Additionally, we describe the current challenges of software architecture devoted to action selection, an analysis not provided in similar reviews of behavioural models for autonomous and social robots. Finally, we present the future directions that these systems can take in the future.

New Approaches to 3D Vision

New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model of the world, the new approaches to 3D vision explored in this issue challenge this assumption. Instead, they investigate the possibility that computer vision, animal navigation, and human vision can rely on partial or distorted models or no model at all. This issue also highlights the implications for artificial intelligence, autonomous vehicles, human perception in virtual and augmented reality, and the treatment of visual disorders, all of which are explored by individual articles. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

Emergent dynamics in a robotic model based on the Caenorhabditis elegans connectome

We analyze the neural dynamics and their relation with the emergent actions of a robotic vehicle that is controlled by a neural network numerical simulation based on the nervous system of the nematode Caenorhabditis elegans. The robot interacts with the environment through a sensor that transmits the information to sensory neurons, while motor neurons outputs are connected to wheels. This is enough to allow emergent robot actions in complex environments, such as avoiding collisions with obstacles. Working with robotic models makes it possible to simultaneously keep track of the dynamics of all the neurons and also register the actions of the robot in the environment in real time, while avoiding the complex technicalities of simulating a real environment. This allowed us to identify several relevant features of the neural dynamics associated with the emergent actions of the robot, some of which have already been observed in biological worms. These results suggest that some basic aspects of behaviors observed in living beings are determined by the underlying structure of the associated neural network.

Grade-control outdoor turning flight of robo-pigeon with quantitative stimulus parameters

Introduction

The robo-pigeon using homing pigeons as a motion carrier has great potential in search and rescue operations due to its superior weight-bearing capacity and sustained flight capabilities. However, before deploying such robo-pigeons, it is necessary to establish a safe, stable, and long-term effective neuro-electrical stimulation interface and quantify the motion responses to various stimuli.

Methods

In this study, we investigated the effects of stimulation variables such as stimulation frequency (SF), stimulation duration (SD), and inter-stimulus interval (ISI) on the turning flight control of robo-pigeons outdoors, and evaluated the efficiency and accuracy of turning flight behavior accordingly.

Results

The results showed that the turning angle can be significantly controlled by appropriately increasing SF and SD. Increasing ISI can significantly control the turning radius of robotic pigeons. The success rate of turning flight control decreases significantly when the stimulation parameters exceed SF > 100 Hz or SD > 5 s. Thus, the robo-pigeon's turning angle from 15 to 55° and turning radius from 25 to 135 m could be controlled in a graded manner by selecting varying stimulus variables.

Discussion

These findings can be used to optimize the stimulation strategy of robo-pigeons to achieve precise control of their turning flight behavior outdoors. The results also suggest that robo-pigeons have potential for use in search and rescue operations where precise control of flight behavior is required.

After 150 years of watching: is there a need for synthetic ethology?

The Darwinian idea of mental continuity is about 150 years old. Although nobody has strongly denied this evolutionary link, both conceptually and practically, relative slow advance has been made by ethology and comparative psychology to quantify mental evolution. Debates on the mechanistic interpretation of cognition often struggle with the same old issues (e.g., associationism vs cognitivism), and in general, experimental methods have made also relative slow progress since the introduction of the puzzle box. In this paper, we illustrate the prevailing issues using examples on 'mental state attribution' and 'perspective taking" and argue that the situation could be improved by the introduction of novel methodological inventions and insights. We suggest that focusing on problem-solving skills and constructing artificial agents that aim to correspond and interact with biological ones, may help to understand the functioning of the mind. We urge the establishment of a novel approach, synthetic ethology, in which researchers take on a practical stance and construct artificial embodied minds relying of specific computational architectures the performance of which can be compared directly to biological agents.

Interactive neurorobotics: Behavioral and neural dynamics of agent interactions

Interactive neurorobotics is a subfield which characterizes brain responses evoked during interaction with a robot, and their relationship with the behavioral responses. Gathering rich neural and behavioral data from humans or animals responding to agents can act as a scaffold for the design process of future social robots. This research seeks to study how organisms respond to artificial agents in contrast to biological or inanimate ones. This experiment uses the novel affordances of the robotic platforms to investigate complex dynamics during minimally structured interactions that would be difficult to capture with classical experimental setups. We then propose a general framework for such experiments that emphasizes naturalistic interactions combined with multimodal observations and complementary analysis pipelines that are necessary to render a holistic picture of the data for the purpose of informing robotic design principles. Finally, we demonstrate this approach with an exemplar rat-robot social interaction task which included simultaneous multi-agent tracking and neural recordings.

Ecology of fear in highly invasive fish revealed by robots

Invasive species threaten biodiversity and ecosystem functioning. We develop an innovative experimental approach, integrating biologically inspired robotics, time-series analysis, and computer vision, to build a detailed profile of the effects of non-lethal stress on the ecology and evolution of mosquitofish (Gambusia holbrooki)—a global pest. We reveal that brief exposures to a robotic predator alter mosquitofish behavior, increasing fear and stress responses, and mitigate the impact of mosquitofish on native tadpoles (Litoria moorei) in a cause-and-effect fashion. Effects of predation risk from the robot carry over to routine activity and feeding rate of mosquitofish weeks after exposure, resulting in weight loss, variation in body shape, and reduction in the fertility of both sexes—impairing survival, reproduction, and ecological success. We capitalize on evolved responses of mosquitofish to reduce predation risk—neglected in biological control practices—and provide scientific foundations for widespread use of state-of-the-art robotics in ecology and evolution research.

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Can robotic predators reveal the vulnerabilities of invasive and pest species?

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Our predator selectively targets invasive fish to protect native amphibians

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Stress from the robot compromises behavior, health, and reproduction of invaders

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We open new frontiers for robotics in ecology, evolution, and biocontrol research

Body Caudal Undulation measured by Soft Sensors and emulated by Soft Artificial Muscles

We propose the use of bio-inspired robotics equipped with soft sensor technologies to gain a better understanding of the mechanics and control of animal movement. Soft robotic systems can be used to generate new hypotheses and uncover fundamental principles underlying animal locomotion and sensory capabilities, which could subsequently be validated using living organisms. Physical models increasingly include lateral body movements, notably back and tail bending, which are necessary for horizontal plane undulation in model systems ranging from fish to amphibians and reptiles. We present a comparative study of the use of physical modeling in conjunction with soft robotics and integrated soft and hyperelastic sensors to monitor local pressures, enabling local feedback control, and discuss issues related to understanding the mechanics and control of undulatory locomotion. A parallel approach combining live animal data with biorobotic physical modeling promises to be beneficial for gaining a better understanding of systems in motion.

Validating a Termite-Inspired Construction Coordination Mechanism Using an Autonomous Robot

Many species of termites build large, structurally complex mounds, and the mechanisms behind this coordinated construction have been a longstanding topic of investigation. Recent work has suggested that humidity may play a key role in the mound expansion of savannah-dwelling Macrotermes species: termites preferentially deposit soil on the mound surface at the boundary of the high-humidity region characteristic of the mound interior, implying a coordination mechanism through environmental feedback where addition of wet soil influences the humidity profile and vice versa. Here we test this potential mechanism physically using a robotic system. Local humidity measurements provide a cue for material deposition. As the analogue of the termite's deposition of wet soil and corresponding local increase in humidity, the robot drips water onto an absorbent substrate as it moves. Results show that the robot extends a semi-enclosed area outward when air is undisturbed, but closes it off when air is disturbed by an external fan, consistent with termite building activity in still vs. windy conditions. This result demonstrates an example of adaptive construction patterns arising from the proposed coordination mechanism, and supports the hypothesis that such a mechanism operates in termites.

Navigation of a Freely Walking Fruit Fly in Infinite Space Using a Transparent Omnidirectional Locomotion Compensator (TOLC)

Animal behavior is an essential element in behavioral neuroscience study. However, most behavior studies in small animals such as fruit flies (Drosophilamelanogaster) have been performed in a limited spatial chamber or by tethering the fly's body on a fixture, which restricts its natural behavior. In this paper, we developed the Transparent Omnidirectional Locomotion Compensator (TOLC) for a freely walking fruit fly without tethering, which enables its navigation in infinite space. The TOLC maintains a position of a fruit fly by compensating its motion using the transparent sphere. The TOLC is capable of maintaining the position error < 1 mm for 90.3% of the time and the heading error < 5° for 80.2% of the time. The inverted imaging system with a transparent sphere secures the space for an additional experimental apparatus. Because the proposed TOLC allows us to observe a freely walking fly without physical tethering, there is no potential injury during the experiment. Thus, the TOLC will offer a unique opportunity to investigate longitudinal studies of a wide range of behavior in an unrestricted walking Drosophila.

Bio-robotic cues show how the Trinidadian guppy male recognises the morphological features of receptive females

Sensory fusion is used by the males of several animal species to discriminate the mating status of females by evaluating their phenotypic traits. The predominant trait used is olfactory cues, and the role of visual cues is not yet fully understood. The ability of Poecilia reticulata males to evaluate females' receptivity based on visual cues was investigated. Guppy males adopt two different mating strategies, courtship displays and forced copulation, towards receptive and pregnant females, respectively. Robotic counterparts mimicking receptive and pregnant females were developed to test whether males relied only on visual information to determine a females' mating status. Exposure to the robotic receptive females evoked courtship behaviours, while forced copulation attempts were more frequent towards the robotic pregnant females. When the robotic fish were simultaneously exposed, regardless of the presence or absence of receptive-female olfactory cues, males expressed their preference for the receptive female. Visual processing showed that fish social interactions played a strategic role in their collection of information, especially when other stimuli were not available. The proposed ethorobotic approach allowed for assessing the role of visual cues in the mating choice of P. reticulata males and highlighting the information processing methods and cognition in aquatic animals.

Beetle-robot hybrid interaction: sex, lateralization and mating experience modulate behavioural responses to robotic cues in the larger grain borer Prostephanus truncatus (Horn)

Ethorobotics, a new fascinating field of biorobotics, proposes the use of robotic replicas as an advanced method for investigating animal behaviour. This novel research approach can also encourage the development of advanced bioinspired robots. In the present study, we investigated the pushing behaviour, a particular display occurring in several beetle species, such as the larger grain borer, Prostephanus truncatus, during both male-female and male-male contexts. We developed a robotic apparatus actuating female and male-mimicking dummies to study if sex, mating experience and asymmetries of robotic cues can modulate the escalation of pushing behaviour. Results showed that the time needed by P. truncatus to react to female-smelling biomimetic dummies was chiefly affected by their mating experience and the dummy odour. This was likely due to reduce waste of costly sperm in mated males during the subsequent sexual interactions. The pushing behaviour was performed longer and with a higher number of acts when virgin females were approached from their right side. More and longer pushing acts were noted when virgin males were approached from their left side. Dedicated neural circuits would likely act in opposite direction in females and males producing population-level lateralized sensory-motor displays, which may be evolved to promote male approaches from the left side of females, thus improving short-distance sex recognition. Overall, this study provides new insights on the behavioural ecology of stored-product beetles, as well as on self-organization and decentralized decision making that can be exploited to develop bioinspired algorithms for task optimization, involving real-world scenarios.

JumpDetector: An automated monitoring equipment for the locomotion of jumping insects

Continuous jumping behavior, a kind of endurance locomotion, plays important roles in insect ecological adaption and survival. However, the methods used for the efficient evaluation of insect jumping behavior remain largely lacking. Here, we developed a locomotion detection system named JumpDetector with automatic trajectory tracking and data analysis to evaluate the jumping of insects. This automated system exhibits more accurate, efficient, and adjustable performance than manual methods. By using this automatic system, we characterized a gradually declining pattern of continuous jumping behavior in 4th-instar nymphs of the migratory locust. We found that locusts in their gregarious phase outperformed locusts in their solitary phase in the endurance jumping locomotion. Therefore, the JumpDetector could be widely used in jumping behavior and endurance locomotion measurement.

Guppies Prefer to Follow Large (Robot) Leaders Irrespective of Own Size

Body size is often assumed to determine how successfully an individual can lead others with larger individuals being better leaders than smaller ones. But even if larger individuals are more readily followed, body size often correlates with specific behavioral patterns and it is thus unclear whether larger individuals are more often followed than smaller ones because of their size or because they behave in a certain way. To control for behavioral differences among differentially-sized leaders, we used biomimetic robotic fish (Robofish) of different sizes. Live guppies (Poecilia reticulata) are known to interact with Robofish in a similar way as with live conspecifics. Consequently, Robofish may serve as a conspecific-like leader that provides standardized behaviors irrespective of its size. We asked whether larger Robofish leaders are preferentially followed and whether the preferences of followers depend on own body size or risk-taking behavior ("boldness"). We found that live female guppies followed larger Robofish leaders in closer proximity than smaller ones and this pattern was independent of the followers' own body size as well as risk-taking behavior. Our study shows a "bigger is better" pattern in leadership that is independent of behavioral differences among differentially-sized leaders, followers' own size and risk-taking behavior.

Animals in Virtual Environments

The core idea in an XR (VR/MR/AR) application is to digitally stimulate one or more sensory systems (e.g. visual, auditory, olfactory) of the human user in an interactive way to achieve an immersive experience. Since the early 2000s biologists have been using Virtual Environments (VE) to investigate the mechanisms of behavior in non-human animals including insects, fish, and mammals. VEs have become reliable tools for studying vision, cognition, and sensory-motor control in animals. In turn, the knowledge gained from studying such behaviors can be harnessed by researchers designing biologically inspired robots, smart sensors, and rnulti-agent artificial intelligence. VE for animals is becoming a widely used application of XR technology but such applications have not previously been reported in the technical literature related to XR. Biologists and computer scientists can benefit greatly from deepening interdisciplinary research in this emerging field and together we can develop new methods for conducting fundamental research in behavioral sciences and engineering. To support our argument we present this review which provides an overview of animal behavior experiments conducted in virtual environments.

Open-source five degree of freedom motion platform for investigating fish-robot interaction

This paper presents the design, construction, operation, and validation of a robotic gantry platform specifically designed for studying fish-robot interaction. The platform has five degrees of freedom to manipulate the three-dimensional position, yaw angle, and the pitch of a lure. Additionally, it has a four-conductor slip ring that allows power and data to be transmitted to the lure for the operation of fins and other actuators that increase realism or act as stimuli to focal fish during an ethorobotic experiment. The design is open-source, low-cost, and includes purpose-built electronics, software, and hardware to make it extensible and customizable for a number of applications with varying requirements.

Avoidance of non-localizable obstacles in echolocating bats: A robotic model

Most objects and vegetation making up the habitats of echolocating bats return a multitude of overlapping echoes. Recent evidence suggests that the limited temporal and spatial resolution of bio-sonar prevents bats from separately perceiving the objects giving rise to these overlapping echoes. Therefore, bats often operate under conditions where their ability to localize obstacles is severely limited. Nevertheless, bats excel at avoiding complex obstacles. In this paper, we present a robotic model of bat obstacle avoidance using interaural level differences and distance to the nearest obstacle as the minimal set of cues. In contrast to previous robotic models of bats, the current robot does not attempt to localize obstacles. We evaluate two obstacle avoidance strategies. First, the Fixed Head Strategy keeps the acoustic gaze direction aligned with the direction of flight. Second, the Delayed Linear Adaptive Law (DLAL) Strategy uses acoustic gaze scanning, as observed in hunting bats. Acoustic gaze scanning has been suggested to aid the bat in hunting for prey. Here, we evaluate its adaptive value for obstacle avoidance when obstacles can not be localized. The robot's obstacle avoidance performance is assessed in two environments mimicking (highly cluttered) experimental setups commonly used in behavioral experiments: a rectangular arena containing multiple complex cylindrical reflecting surfaces and a corridor lined with complex reflecting surfaces. The results indicate that distance to the nearest object and interaural level differences allows steering the robot clear of obstacles in environments that return non-localizable echoes. Furthermore, we found that using acoustic gaze scanning reduced performance, suggesting that gaze scanning might not be beneficial under conditions where the animal has limited access to angular information, which is in line with behavioral evidence.

A Comparison of Individual Learning and Social Learning in Zebrafish Through an Ethorobotics Approach

Social learning is ubiquitous across the animal kingdom, where animals learn from group members about predators, foraging strategies, and so on. Despite its prevalence and adaptive benefits, our understanding of social learning is far from complete. Here, we study observational learning in zebrafish, a popular animal model in neuroscience. Toward fine control of experimental variables and high consistency across trials, we developed a novel robotics-based experimental test paradigm, in which a robotic replica demonstrated to live subjects the correct door to join a group of conspecifics. We performed two experimental conditions. In the individual training condition, subjects learned the correct door without the replica. In the social training condition, subjects observed the replica approaching both the incorrect door, to no effect, and the correct door, which would open after spending enough time close to it. During these observations, subjects could not actively follow the replica. Zebrafish increased their preference for the correct door over the course of 20 training sessions, but we failed to identify evidence of social learning, whereby we did not register significant differences in performance between the individual and social training conditions. These results suggest that zebrafish may not be able to learn a route by observation, although more research comparing robots to live demonstrators is needed to substantiate this claim.

A review on animal-robot interaction: from bio-hybrid organisms to mixed societies

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.

Encoding lateralization of jump kinematics and eye use in a locust via bio-robotic artifacts

The effect of previous exposure to lateral sensory stimuli in shaping the response to subsequent symmetric stimuli represents an important overlooked issue in neuroethology, with special reference to arthropods. In this research, we investigated the hypothesis to 'programme' jumping escape direction as well as surveillance orientation in young and adult individuals of Locusta migratoria as an adaptive consequence of prior exposure to directional-biased predator approaches generated by a robotic leopard gecko representing Eublepharis macularius The manipulation of the jumping escape direction was successfully achieved in young locusts, although young L. migratoria did not exhibit innately lateralized jumping escapes. Jumping escape direction was also successfully manipulated in adult locusts, which exhibited innate lateralized jumping escape at the individual level. The innate lateralization of each instar of L. migratoria in using a preferential eye during surveillance was not affected by prior lateralized exposure to the robotic gecko. Our results indicate a high plasticity of the escape motor outputs that are occurring almost in real time with the perceived stimuli, making them greatly adaptable and compliant to environmental changes in order to be effective and reliable. In addition, surveillance lateralization innately occurs at population level in each instar of L. migratoria Therefore, its low forgeability by environmental factors would avoid disorganization at swarm level and improve swarm coordination during group tasks. These findings are consistent with the fact that, as in vertebrates, in insects the right hemisphere is specialized in controlling fear and escape functions.

Towards Computational Models and Applications of Insect Visual Systems for Motion Perception: A Review

Motion perception is a critical capability determining a variety of aspects of insects' life, including avoiding predators, foraging, and so forth. A good number of motion detectors have been identified in the insects' visual pathways. Computational modeling of these motion detectors has not only been providing effective solutions to artificial intelligence, but also benefiting the understanding of complicated biological visual systems. These biological mechanisms through millions of years of evolutionary development will have formed solid modules for constructing dynamic vision systems for future intelligent machines. This article reviews the computational motion perception models originating from biological research on insects' visual systems in the literature. These motion perception models or neural networks consist of the looming-sensitive neuronal models of lobula giant movement detectors (LGMDs) in locusts, the translation-sensitive neural systems of direction-selective neurons (DSNs) in fruit flies, bees, and locusts, and the small-target motion detectors (STMDs) in dragonflies and hoverflies. We also review the applications of these models to robots and vehicles. Through these modeling studies, we summarize the methodologies that generate different direction and size selectivity in motion perception. Finally, we discuss multiple systems integration and hardware realization of these bio-inspired motion perception models.

Spiking Neurons Integrating Visual Stimuli Orientation and Direction Selectivity in a Robotic Context

Visual motion detection is essential for the survival of many species. The phenomenon includes several spatial properties, not fully understood at the level of a neural circuit. This paper proposes a computational model of a visual motion detector that integrates direction and orientation selectivity features. A recent experiment in the Drosophila model highlights that stimulus orientation influences the neural response of direction cells. However, this interaction and the significance at the behavioral level are currently unknown. As such, another objective of this article is to study the effect of merging these two visual processes when contextualized in a neuro-robotic model and an operant conditioning procedure. In this work, the learning task was solved using an artificial spiking neural network, acting as the brain controller for virtual and physical robots, showing a behavior modulation from the integration of both visual processes.

Using a robotic fish to investigate individual differences in social responsiveness in the guppy

Responding towards the actions of others is one of the most important behavioural traits whenever animals of the same species interact. Mutual influences among interacting individuals may modulate the social responsiveness seen and thus make it often difficult to study the level and individual variation in responsiveness. Here, open-loop biomimetic robots that provide standardized, non-interactive social cues can be a useful tool. These robots are not affected by the live animal's actions but are assumed to still represent valuable and biologically relevant social cues. As this assumption is crucial for the use of biomimetic robots in behavioural studies, we hypothesized (i) that meaningful social interactions can be assumed if live animals maintain individual differences in responsiveness when interacting with both a biomimetic robot and a live partner. Furthermore, to study the level of individual variation in social responsiveness, we hypothesized (ii) that individual differences should be maintained over the course of multiple tests with the robot. We investigated the response of live guppies (Poecilia reticulata) when allowed to interact either with a biomimetic open-loop-controlled fish robot-'Robofish'-or with a live companion. Furthermore, we investigated the responses of live guppies when tested three times with Robofish. We found that responses of live guppies towards Robofish were weaker compared with those of a live companion, most likely as a result of the non-interactive open-loop behaviour of Robofish. Guppies, however, were consistent in their individual responses between a live companion and Robofish, and similar individual differences in response towards Robofish were maintained over repeated testing even though habituation to the test environment was detectable. Biomimetic robots like Robofish are therefore a useful tool for the study of social responsiveness in guppies and possibly other small fish species.

Bioinspired magnetoreception and navigation using magnetic signatures as waypoints

Diverse taxa use Earth's magnetic field in conjunction with other sensory modalities to accomplish navigation tasks ranging from local homing to long-distance migration across continents and ocean basins. However, despite extensive research, the mechanisms that underlie animal magnetoreception are not clearly understood, and how animals use Earth's magnetic field to navigate is an active area of investigation. Concurrently, Earth's magnetic field offers a signal that engineered systems can leverage for navigation in environments where man-made systems such as GPS are unavailable or unreliable. Using a proxy for Earth's magnetic field, and inspired by migratory animal behavior, this work implements a behavioral strategy that uses combinations of magnetic field properties as rare or unique signatures that mark specific locations. Using a discrete number of these signatures as goal waypoints, the strategy navigates through a closed set of points several times in a variety of environmental conditions, and with various levels of sensor noise. The results from this engineering/quantitative biology approach support existing notions that some animals may use combinations of magnetic properties as navigational markers, and provides insights into features and constraints that would enable navigational success or failure. The findings also offer insights into how autonomous engineered platforms might be designed to leverage the magnetic field as a navigational resource.

Insights into the Social Behavior of Surface and Cave-Dwelling Fish (Poecilia mexicana) in Light and Darkness through the Use of a Biomimetic Robot

Biomimetic robots (BRs) are becoming more common in behavioral research and, if they are accepted as conspecifics, allow for new forms of experimental manipulations of social interactions. Nevertheless, it is often not clear which cues emanating from a BR are actually used as communicative signals and how species or populations with different sensory makeups react to specific types of BRs. We herein present results from experiments using two populations of livebearing fishes that differ in their sensory capabilities. In the South of Mexico, surface-dwelling mollies (Poecilia mexicana) successfully invaded caves and adapted to dark conditions. While almost without pigment, these cave mollies possess smaller but still functional eyes. Although previous studies found cave mollies to show reduced shoaling preferences with conspecifics in light compared to surface mollies, it is assumed that they possess specialized adaptations to maintain some kind of sociality also in their dark habitats. By testing surface- and cave-dwelling mollies with RoboFish, a BR made for use in laboratory experiments with guppies and sticklebacks, we asked to what extent visual and non-visual cues play a role in their social behavior. Both cave- and surface-dwelling mollies followed the BR as well as a live companion when tested in light. However, when tested in darkness, only surface-dwelling fish were attracted by a live conspecific, whereas cave-dwelling fish were not. Neither cave- nor surface-dwelling mollies were attracted to RoboFish in darkness. This is the first study to use BRs for the investigation of social behavior in mollies and to compare responses to BRs both in light and darkness. As our RoboFish is accepted as conspecific by both used populations of the Atlantic molly only under light conditions but not in darkness, we argue that our replica is providing mostly visual cues.

Bioinspired magnetic reception and multimodal sensing

Several animals use Earth's magnetic field in concert with other sensor modes to accomplish navigational tasks ranging from local homing to continental scale migration. However, despite extensive research, animal magnetic reception remains poorly understood. Similarly, the Earth's magnetic field offers a signal that engineered systems can leverage to navigate in environments where man-made positioning systems such as GPS are either unavailable or unreliable. This work uses a behavioral strategy inspired by the migratory behavior of sea turtles to locate a magnetic goal and respond to wind when it is present. Sensing is performed using a number of distributed sensors. Based on existing theoretical biology considerations, data processing is performed using combinations of circles and ellipses to exploit the distributed sensing paradigm. Agent-based simulation results indicate that this approach is capable of using two separate magnetic properties to locate a goal from a variety of initial conditions in both noiseless and noisy sensory environments. The system's ability to locate the goal appears robust to noise at the cost of overall path length.

Unlocking neural complexity with a robotic key

Complex brains evolved in order to comprehend and interact with complex environments in the real world. Despite significant progress in our understanding of perceptual representations in the brain, our understanding of how the brain carries out higher level processing remains largely superficial. This disconnect is understandable, since the direct mapping of sensory inputs to perceptual states is readily observed, while mappings between (unknown) stages of processing and intermediate neural states is not. We argue that testing theories of higher level neural processing on robots in the real world offers a clear path forward, since (1) the complexity of the neural robotic controllers can be staged as necessary, avoiding the almost intractable complexity apparent in even the simplest current living nervous systems; (2) robotic controller states are fully observable, avoiding the enormous technical challenge of recording from complete intact brains; and (3) unlike computational modelling, the real world can stand for itself when using robots, avoiding the computational intractability of simulating the world at an arbitrary level of detail. We suggest that embracing the complex and often unpredictable closed-loop interactions between robotic neuro-controllers and the physical world will bring about deeper understanding of the role of complex brain function in the high-level processing of information and the control of behaviour.

Validating a model for detecting magnetic field intensity using dynamic neural fields

Several animals use properties of Earth's magnetic field as a part of their navigation toolkit to accomplish tasks ranging from local homing to continental migration. Studying these behaviors has led to the postulation of both a magnetite-based sense, and a chemically based radical-pair mechanism. Several researchers have proposed models aimed at both understanding these mechanisms, and offering insights into future physiological experiments. The present work mathematically implements a previously developed conceptual model for sensing and processing magnetite-based magnetosensory feedback by using dynamic neural fields, a computational neuroscience tool for modeling nervous system dynamics and processing. Results demonstrate the plausibility of the conceptual model's predictions. Specifically, a population of magnetoreceptors in which each individual can only sense directional information can encode magnetic intensity en masse. Multiple populations can encode both magnetic direction, and intensity, two parameters that several animals use in their navigational toolkits. This work can be expanded to test other magnetoreceptor models.

Modular Reactive Neurocontrol for Biologically Inspired Walking Machines

A neurocontroller is described which generates the basic locomotion and controls the sensor-driven behavior of a four-legged and a six-legged walking machine. The controller utilizes discrete-time neurodynamics, and is of modular structure. One module is for processing sensor signals, one is a neural oscillator network serving as a central pattern generator, and the third one is a so-called velocity regulating network. These modules are small and their structures and their functionalities are analyzable. In combination, they enable the machines to autonomously explore an unknown environment, to avoid obstacles, and to escape from corners or deadlock situations. The neurocontroller was developed and tested first using a physical simulation environment, and then it was successfully transferred to the physical walking machines. Locomotion is based on a gait where the diagonal legs are paired and move together, e.g. trot gait for the four-legged walking machine and tripod gait for the six-legged walking machine. The controller developed is universal in the sense that it can easily be adapted to different types of even-legged walking machines without changing the internal structure and its parameters.

RoboFish: increased acceptance of interactive robotic fish with realistic eyes and natural motion patterns by live Trinidadian guppies

In recent years, simple biomimetic robots have been increasingly used in biological studies to investigate social behavior, for example collective movement. Nevertheless, a big challenge in developing biomimetic robots is the acceptance of the robotic agents by live animals. In this contribution, we describe our recent advances with regard to the acceptance of our biomimetic RoboFish by live Trinidadian guppies (Poecilia reticulata). We provide a detailed technical description of the RoboFish system and show the effect of different appearance, motion patterns and interaction modes on the acceptance of the artificial fish replica. Our results indicate that realistic eye dummies along with natural motion patterns significantly improve the acceptance level of the RoboFish. Through the interactive behaviors, our system can be adjusted to imitate different individual characteristics of live animals, which further increases the bandwidth of possible applications of our RoboFish for the study of animal behavior.

Sensorimotor Model of Obstacle Avoidance in Echolocating Bats

Bat echolocation is an ability consisting of many subtasks such as navigation, prey detection and object recognition. Understanding the echolocation capabilities of bats comes down to isolating the minimal set of acoustic cues needed to complete each task. For some tasks, the minimal cues have already been identified. However, while a number of possible cues have been suggested, little is known about the minimal cues supporting obstacle avoidance in echolocating bats. In this paper, we propose that the Interaural Intensity Difference (IID) and travel time of the first millisecond of the echo train are sufficient cues for obstacle avoidance. We describe a simple control algorithm based on the use of these cues in combination with alternating ear positions modeled after the constant frequency bat Rhinolophus rouxii. Using spatial simulations (2D and 3D), we show that simple phonotaxis can steer a bat clear from obstacles without performing a reconstruction of the 3D layout of the scene. As such, this paper presents the first computationally explicit explanation for obstacle avoidance validated in complex simulated environments. Based on additional simulations modelling the FM bat Phyllostomus discolor, we conjecture that the proposed cues can be exploited by constant frequency (CF) bats and frequency modulated (FM) bats alike. We hypothesize that using a low level yet robust cue for obstacle avoidance allows bats to comply with the hard real-time constraints of this basic behaviour.

Echolocating bats can fly through complex environments in complete darkness. Swift and apparently effortless obstacle avoidance is the most fundamental function supported by biosonar. Despite this, we still do not know which acoustic cues, from among the many possible cues, bats actually exploit while avoiding obstacles. In this paper, we show using spatial simulations (2D and 3D) that the Interaural Intensity Difference (IID) and travel time of the first millisecond of the echo train in combination with alternating ear positions provide robust and reliable cues for obstacle avoidance. Simulating the echoes received by a flying bat, we show that simple phonotaxis can steer a bat clear from obstacles without performing 3D reconstruction of the layout of the scene. As such, this paper presents the first computationally explicit explanation for obstacle avoidance in realistic and complex 3D environments. We hypothesize that using low level yet robust cues for obstacle avoidance allows bats to comply with the hard real-time constraints of this basic behaviour.

Posture affects how robots and infants map words to objects

For infants, the first problem in learning a word is to map the word to its referent; a second problem is to remember that mapping when the word and/or referent are again encountered. Recent infant studies suggest that spatial location plays a key role in how infants solve both problems. Here we provide a new theoretical model and new empirical evidence on how the body – and its momentary posture – may be central to these processes. The present study uses a name-object mapping task in which names are either encountered in the absence of their target (experiments 1–3, 6 & 7), or when their target is present but in a location previously associated with a foil (experiments 4, 5, 8 & 9). A humanoid robot model (experiments 1–5) is used to instantiate and test the hypothesis that body-centric spatial location, and thus the bodies’ momentary posture, is used to centrally bind the multimodal features of heard names and visual objects. The robot model is shown to replicate existing infant data and then to generate novel predictions, which are tested in new infant studies (experiments 6–9). Despite spatial location being task-irrelevant in this second set of experiments, infants use body-centric spatial contingency over temporal contingency to map the name to object. Both infants and the robot remember the name-object mapping even in new spatial locations. However, the robot model shows how this memory can emerge –not from separating bodily information from the word-object mapping as proposed in previous models of the role of space in word-object mapping – but through the body’s momentary disposition in space.

Computational cognitive models of spatial memory in navigation space: a review

Spatial memory refers to the part of the memory system that encodes, stores, recognizes and recalls spatial information about the environment and the agent's orientation within it. Such information is required to be able to navigate to goal locations, and is vitally important for any embodied agent, or model thereof, for reaching goals in a spatially extended environment. In this paper, a number of computationally implemented cognitive models of spatial memory are reviewed and compared. Three categories of models are considered: symbolic models, neural network models, and models that are part of a systems-level cognitive architecture. Representative models from each category are described and compared in a number of dimensions along which simulation models can differ (level of modeling, types of representation, structural accuracy, generality and abstraction, environment complexity), including their possible mapping to the underlying neural substrate. Neural mappings are rarely explicated in the context of behaviorally validated models, but they could be useful to cognitive modeling research by providing a new approach for investigating a model's plausibility. Finally, suggested experimental neuroscience methods are described for verifying the biological plausibility of computational cognitive models of spatial memory, and open questions for the field of spatial memory modeling are outlined.