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Ants are capable of learning long visually guided foraging routes with limited neural resources. The visual scene memory needed for this behaviour is mediated by the mushroom bodies; an insect brain region important for learning and memory. In a visual navigation context, the mushroom bodies are theorised to act as familiarity detectors, guiding ants to views that are similar to those previously learned when first travelling along a foraging route. Evidence from behavioural experiments, computational studies and brain lesions all support this idea. Here we further investigate the role of mushroom bodies in visual navigation with a spiking neural network model learning complex natural scenes. By implementing these networks in GeNN-a library for building GPU accelerated spiking neural networks-we were able to test these models offline on an image database representing navigation through a complex outdoor natural environment, and also online embodied on a robot. The mushroom body model successfully learnt a large series of visual scenes (400 scenes corresponding to a 27 m route) and used these memories to choose accurate heading directions during route recapitulation in both complex environments. Through analysing our model's Kenyon cell (KC) activity, we were able to demonstrate that KC activity is directly related to the respective novelty of input images. Through conducting a parameter search we found that there is a non-linear dependence between optimal KC to visual projection neuron (VPN) connection sparsity and the length of time the model is presented with an image stimulus. The parameter search also showed training the model on lower proportions of a route generally produced better accuracy when testing on the entire route. We embodied the mushroom body model and comparator visual navigation algorithms on a Quanser Q-car robot with all processing running on an Nvidia Jetson TX2. On a 6.5 m route, the mushroom body model had a mean distance to training route (error) of 0.144 ± 0.088 m over 5 trials, which was performance comparable to standard visual-only navigation algorithms. Thus, we have demonstrated that a biologically plausible model of the ant mushroom body can navigate complex environments both in simulation and the real world. Understanding the neural basis of this behaviour will provide insight into how neural circuits are tuned to rapidly learn behaviourally relevant information from complex environments and provide inspiration for creating bio-mimetic computer/robotic systems that can learn rapidly with low energy requirements.

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Controlling robots in space with necessarily low material and structural stiffness is quite challenging at least in part due to the resulting very low structural resonant frequencies or natural vibration. The frequencies are sometimes so low that the very act of controlling the robot with medium or high bandwidth controllers leads to excitation of resonant vibrations in the robot appendages. Biomimetics or biomimicry emulates models, systems, and elements of nature for solving such complex problems. Recent seminal publications have re-introduced the viability of optimal command shaping, and one recent instantiation mimics baseball pitching to propose control of highly flexible space robots. The readership will find a perhaps dizzying array of thirteen decently performing alternatives in the literature but could be left bereft selecting a method(s) deemed to be best suited for a particular application. Bio-inspired control of space robotics is presented in a quite substantial (perhaps not comprehensive) comparison, and the conclusions of this study indicate the three top performing methods based on minimizing control effort (i.e., fuel) usage, tracking error mean, and tracking error deviation, where 96%, 119%, and 80% performance improvement, respectively, are achieved.

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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.

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[This corrects the article DOI: 10.3389/frobt.2023.1075634.].

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This work explores the recent research conducted towards the development of novel classes of devices in wearable and implantable medical applications allowed by the introduction of the soft robotics approach. In the medical field, the need for materials with mechanical properties similar to biological tissues is one of the first considerations that arises to improve comfort and safety in the physical interaction with the human body. Thus, soft robotic devices are expected to be able of accomplishing tasks no traditional rigid systems can do. In this paper, we describe future perspectives and possible routes to address scientific and clinical issues still hampering the accomplishment of ideal solutions in clinical practice.

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Despite the broadly applicable potential in the bioelectronics, organic/inorganic material-based bioelectronics have some limitations such as hard stiffness and low biocompatibility. To overcome these limitations, hydrogels capable of bridging the interface and connecting biological materials and electronics have been investigated for development of hydrogel bioelectronics. Although hydrogel bioelectronics have shown unique properties including flexibility and biocompatibility, there are still limitations in developing novel hydrogel bioelectronics using only hydrogels such as their low electrical conductivity and structural stability. As an alternative solution to address these issues, studies on the development of biohybrid hydrogels that incorporating nanomaterials into the hydrogels have been conducted for bioelectronic applications. Nanomaterials complement the shortcomings of hydrogels for bioelectronic applications, and provide new functionality in biohybrid hydrogel bioelectronics. In this review, we provide the recent studies on biohybrid hydrogels and their bioelectronic applications. Firstly, representative nanomaterials and hydrogels constituting biohybrid hydrogels are provided, and next, applications of biohybrid hydrogels in bioelectronics categorized in flexible/wearable bioelectronic devices, tissue engineering, and biorobotics are discussed with recent studies. In conclusion, we strongly believe that this review provides the latest knowledge and strategies on hydrogel bioelectronics through the combination of nanomaterials and hydrogels, and direction of future hydrogel bioelectronics.

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In recent years, bio-inspired robots have shaped numerous domains of technical and scientific production. Bio-inspired robots can now be found in all areas of industry, medicine, architecture, and even culture. Despite the wealth of historiographic and philosophical studies published on this topic, a philosophical investigation of the mimetic principle used in bio-robotics is still missing. In this paper, I will ask a simple question: what is the role of biomimetic and bio-inspired processes in the different practices of bio-robotics? In the following pages, I will first make some conceptual order. I discuss the differences between several bio-inspired disciplines to clearly identify the biomimetic principle. Second, I introduce the discussion on the necessity of imitating nature in early twentieth-century bio-robotics. Third, I state the broader philosophical issue at stake in the debate on the biomimetic principle: the model-world relation, as discussed in the philosophical literature on models and idealization. In section 4, I address several emblematic case studies in which the imitation of nature, variously declined, is fundamental for producing knowledge, thus providing cursory taxonomy of the biomimetic principle. In the conclusion, I will come back to the elephant in the room and suggest how to tackle it further in a fruitful manner. As a broader result, my proposed taxonomy might be used by historians of science and technology as a starting point for historicizing the different practices of current bio-robotics as well as by philosophers to further problematize the various philosophical frameworks that have been accepted and developed in bio-robotics.

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The development of robots as tools for biological research, sometimes termed "biorobotics", has grown rapidly in recent years, fueled by the proliferation of miniaturized computation and advanced manufacturing techniques. Much of this work is focused on the use of robots as biomechanical models for natural systems. But, increasingly, biomimetic robots are being employed to interact directly with animals, as component parts of ethology studies in the field and behavioral neuroscience studies in the laboratory. While it has been possible to mechanize and automate animal behavior experiments for decades, only recently has there been the prospect of creating at-scale robotic animals containing the sensing, autonomy and actuation necessary for complex, life-like interaction. This not only opens up new avenues of enquiry, but also provides important ways to improve animal welfare, both by reducing or replacing the use of animal subjects, and by minimizing animal distress (if robots are used judiciously). This article will discuss the current state of the art in robotic lab rats, providing perspective on where research could be directed to enable the safe and effective use of biorobotic animals.

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In this paper, we ask one fairly simple question: to what extent can biorobotics be sensibly qualified as science? The answer clearly depends on what 'science' means and whether what is actually done in biorobotics corresponds to this meaning. To respond to this question, we will deploy the distinction between science and so-called technoscience, and isolate different kinds of objects of inquiry in biorobotics research. Capitalising on the distinction between 'proximal' and 'distal' biorobotic hypotheses, we will argue that technoscientific biorobotic studies address proximal hypotheses, whilst scientific biorobotic studies address distal hypotheses. As a result, we argue that bioroboticians can be both considered as scientists and technoscientists and that this is one of the main payoffs of biorobotics. Indeed, technoscientists play an extremely important role in 21st-century culture and in the current critical production of knowledge. Today's world is increasingly technological, or rather, it is a bio-hybrid system in which the biological and the technological are mixed. Therefore, studying the behaviour of robotic systems and the phenomena of animal-robot interaction means analysing, understanding, and shaping our world. Indeed, in the conclusion of the paper, we broadly reflect on the philosophical and disciplinary payoff of seeing biorobotics as a science and/or technoscience for the increasingly bio-hybrid and technical world of the 21st century.

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The brain forms unified, coherent, and accurate percepts of events occurring in the environment by integrating information from multiple senses through the process of multisensory integration. The neural mechanisms underlying this process, its development and its maturation in a multisensory environment are yet to be properly understood. Numerous psychophysical studies suggest that the multisensory cue integration process follows the principle of Bayesian estimation, where the contributions of individual sensory modalities are proportional to the relative reliabilities of the different sensory stimuli. In this article I hypothesize that experience dependent crossmodal synaptic plasticity may be a plausible mechanism underlying development of multisensory cue integration. I test this hypothesis via a computational model that implements Bayesian multisensory cue integration using reliability-based cue weighting. The model uses crossmodal synaptic plasticity to capture stimulus statistics within synaptic weights that are adapted to reflect the relative reliabilities of the participating stimuli. The model is embodied in a simulated robotic agent that learns to localize an audio-visual target by integrating spatial location cues extracted from of auditory and visual sensory modalities. Results of multiple randomized target localization trials in simulation indicate that the model is able to learn modality-specific synaptic weights proportional to the relative reliabilities of the auditory and visual stimuli. The proposed model with learned synaptic weights is also compared with a maximum-likelihood estimation model for cue integration via regression analysis. Results indicate that the proposed model reflects maximum-likelihood estimation.

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The industrial revolution 4.0 has led to a burst in the development of robotic automation and platforms to increase productivity in the industrial and health domains. Hence, there is a necessity for the design and production of smart and multi-functional tools, which combine several cutting-edge technologies, including additive manufacturing and smart control systems. In the current article, a novel multi-functional biomimetic soft actuator with a pneumatic motion system was designed and fabricated by combining different additive manufacturing techniques. The developed actuator was bioinspired by the natural kinematics, namely the motion mechanism of worms, and was designed to imitate the movement of a human finger. Furthermore, due to its modular design and the ability to adapt the actuator's external covers depending on the requested task, this actuator is suitable for a wide range of applications, from soft (i.e., fruit grasping) or industrial grippers to medical exoskeletons for patients with mobility difficulties and neurological disorders. In detail, the motion system operates with two pneumatic chambers bonded to each other and fabricated from silicone rubber compounds molded with additively manufactured dies made of polymers. Moreover, the pneumatic system offers multiple-degrees-of-freedom motion and it is capable of bending in the range of -180° to 180°. The overall pneumatic system is protected by external covers made of 3D printed components whose material could be changed from rigid polymer for industrial applications to thermoplastic elastomer for complete soft robotic applications. In addition, these 3D printed parts control the angular range of the actuator in order to avoid the reaching of extreme configurations. Finally, the bio-robotic actuator is electronically controlled by PID controllers and its real-time position is monitored by a one-axis soft flex sensor which is embedded in the actuator's configuration.

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In so-called ethorobotics and robot-supported social cognitive neurosciences, robots are used as scientific tools to study animal behavior and cognition. Building on previous epistemological analyses of biorobotics, in this article it is argued that these two research fields, widely differing from one another in the kinds of robots involved and in the research questions addressed, share a common methodology, which significantly differs from the "synthetic method" that, until recently, dominated biorobotics. The methodological novelty of this strategy, the research opportunities that it opens, and the theoretical and technological challenges that it gives rise to, will be discussed with reference to the peculiarities of the two research fields. Some broad methodological issues related to the generalization of results concerning robot-animal interaction to theoretical conclusions on animal-animal interaction will be identified and discussed.

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The locomotion of soft snake robots is dependent on frictional interactions with the environment. Frictional anisotropy is a morphological characteristic of snakeskin that allows snakes to engage selectively with surfaces and generate propulsive forces. The prototypical slithering gait of most snakes is lateral undulation, which requires a significant lateral resistance that is lacking in artificial skins of existing soft snake robots. We designed a set of kirigami lattices with curvilinearly-arranged cuts to take advantage of in-plane rotations of the 3D structures when wrapped around a soft bending actuator. By changing the initial orientation of the scales, the kirigami skin produces high lateral friction upon engagement with surface asperities, with lateral to cranial anisotropic friction ratios above 4. The proposed design increased the overall velocity of the soft snake robot more than fivefold compared to robots without skin.

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OBJECTIVE: Excessive stress and motion at the L5-S1 level can lead to degenerative changes, especially in patients with posterior instrumentation suprajacent to L5. Attention has turned to utilization of L5-S1 anterior lumbar interbody fusion (ALIF) to stabilize the lumbosacral junction. However, questions remain regarding the effectiveness of stand-alone ALIF in the setting of prior posterior instrumented fusions terminating at L5. The purpose of this study was to assess the biomechanical stability of an L5-S1 ALIF with increasing lengths of posterior thoracolumbar constructs. METHODS: Seven human cadaveric spines (T9-sacrum) were instrumented with pedicle screws from T10 to L5 and mounted to a 6 degrees-of-freedom robot. Posterior fusion construct lengths (T10-L5, T12-L5, L2-5, and L4-5) were instrumented to each specimen, and torque-fusion level relationships were determined for each construct in flexion-extension, axial rotation, and lateral bending. A stand-alone L5-S1 ALIF was then instrumented, and L5-S1 motion was measured as increasing pure moments (2 to 12 Nm) were applied. Motion reduction was calculated by comparing L5-S1 motion across the ALIF and non-ALIF states. RESULTS: The average motion at L5-S1 in axial rotation, flexion-extension, and lateral bending was assessed for each fusion construct with and without ALIF. After adding ALIF to a posterior fusion, L5-S1 motion was significantly reduced relative to the non-ALIF state in all but one fused surgical condition (p < 0.05). Longer fusions with ALIF produced larger L5-S1 motions, and in some cases resulted in motions higher than native state motion. CONCLUSIONS: Posterior fusion constructs up to L4-5 could be appropriately stabilized by a stand-alone L5-S1 ALIF when using a nominal threshold of 80% reduction in native motion as a potential positive indicator of fusion. The results of this study allow conclusions to be drawn from a biomechanical standpoint; however, the clinical implications of these data are not well defined. These findings, when taken in appropriate clinical context, can be used to better guide clinicians seeking to treat L5-S1 pathology in patients with prior posterior thoracolumbar constructs.

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Biorobotics is increasingly attracting engineers worldwide, due to the high impact this field can have on the society. Biorobotics aims at imitating or taking inspiration from mechanisms and strategies evolved by animals, including their locomotion abilities in real scenarios, such as swimming, running, crawling, and flying. However, the development of skin-mimicking structures, allowing protection without hindering artifacts' movements, has been rarely addressed. Skin-mimicking structures play a key role for biomimetic robots that have to move in unstructured environments. Currently most of the skin used for robots in engineering adopts soft materials or bellow structures to enable both structural deformation and protection. However, the elastic nature of the former can produce support failure and increasing strain with deformation, while the humpy surface of the latter reduces the interactive performance with the environment. Herein, we designed a novel compliant structure for biorobots' skin, fabricated through a special configuration of both soft and rigid materials to reproduce attributes provided by natural epithelial structures. The presented skin has a simple fabrication process, as well as it is cost effective. The structure of this skin includes a thin conical shape where rigid iron rings are wrapped by soft polyester fabrics, allowing a theoretically zero elastic modulus when bended and stretched. The dimension of fabrics was specified to allow rigid rings having a certain range of free rotation and translation. The possibility of free bending and stretching of the structure was implemented by overcoming low sliding friction of adjacent rings. To empirically test the effectiveness of the proposed structure, a model, including 20 segments, was also fabricated. Experimental results from the bending tests, both in aerial and underwater environments, as well as from the folding tests, demonstrated the successful performance of the skin prototype in terms of low resistance and energy consumption. Finally, the proposed highly compliant structural skin was mounted and tested on a fish robot previously developed by authors, to further show its effectiveness.

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Facing the threat of rapidly worsening water quality, there is an urgent need to develop novel approaches of monitoring its global supplies and early detection of environmental fluctuations. Global warming, urban growth and other factors have threatened not only the freshwater supply but also the well-being of many species inhabiting it. Traditionally, laboratory-based studies can be both time and money consuming and so, the development of a real-time, continuous monitoring method has proven necessary. The use of autonomous, self-actualizing entities became an efficient way of monitoring the environment. The Microbial Fuel Cells (MFC) will be investigated as an alternative energy source to allow for these entities to self-actualize. This concept has been improved with the use of various lifeforms in the role of biosensors in a structure called "biohybrid" which we aim to develop further within the framework of project Robocoenosis relying on animal-robot interaction. We introduce a novel concept of a fully autonomous biohybrid agent with various lifeforms in the role of biosensors. Herein, we identify most promising organisms in the context of underwater robotics, among others Dreissena polymorpha, Anodonta cygnaea, Daphnia sp. and various algae. Special focus is placed on the "ecosystem hacking" based on their interaction with the electronic parts. This project uses Austrian lakes of various trophic levels (Millstättersee, Hallstättersee and Neusiedlersee) as case studies and as a "proof of concept".

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This review article aims to address common research questions in hexapod robotics. How can we build intelligent autonomous hexapod robots that can exploit their biomechanics, morphology, and computational systems, to achieve autonomy, adaptability, and energy efficiency comparable to small living creatures, such as insects? Are insects good models for building such intelligent hexapod robots because they are the only animals with six legs? This review article is divided into three main sections to address these questions, as well as to assist roboticists in identifying relevant and future directions in the field of hexapod robotics over the next decade. After an introduction in section (1), the sections will respectively cover the following three key areas: (2) biomechanics focused on the design of smart legs; (3) locomotion control; and (4) high-level cognition control. These interconnected and interdependent areas are all crucial to improving the level of performance of hexapod robotics in terms of energy efficiency, terrain adaptability, autonomy, and operational range. We will also discuss how the next generation of bioroboticists will be able to transfer knowledge from biology to robotics and vice versa.

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On the roadmap to building completely autonomous artificial bio-robots, all major aspects of robotic functions, namely, energy generation, processing, sensing, and actuation, need to be self-sustainable and function in the biological realm. Microbial Fuel Cells (MFCs) provide a platform technology for achieving this goal. In a series of experiments, we demonstrate that MFCs can be used as living, autonomous sensors in robotics. In this work, we focus on thermal sensing that is akin to thermoreceptors in mammalian entities. We therefore designed and tested an MFC-based thermosensor system for utilization within artificial bio-robots such as EcoBots. In open-loop sensor characterization, with a controlled load resistance and feed rate, the MFC thermoreceptor was able to detect stimuli of 1 min directed from a distance of 10 cm causing a temperature rise of ∼1°C at the thermoreceptor. The thermoreceptor responded to continuous stimuli with a minimum interval of 384 s. In a practical demonstration, a mobile robot was fitted with two artificial thermosensors, as environmental thermal detectors for thermotactic application, mimicking thermotaxis in biology. In closed-loop applications, continuous thermal stimuli were detected at a minimum time interval of 160 s, without the need for complete thermoreceptor recovery. This enabled the robot to detect thermal stimuli and steer away from a warmer thermal source within the rise of 1°C. We envision that the thermosensor can be used for future applications in robotics, including as a potential sensor mechanism for maintaining thermal homeostasis.

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In so-called interactive biorobotics, robotic models of living systems interact with animals in controlled experimental settings. By observing how the focal animal reacts to the stimuli delivered by the robot, one tests hypotheses concerning the determinants of animal behaviour in social contexts. Building on previous methodological reconstructions of interactive biorobotics, this article reflects on the claim, made by several authors in the field, that this strategy may enable one to explain social phenomena in animals. The answer offered here will be negative: interactive biorobotics does not contribute to the explanation of social phenomena. However, it may greatly contribute to the study of animal behaviour by creating social phenomena in the sense discussed by Ian Hacking, i.e. by precisely defining new phenomena to be explained. It will be also suggested that interactive biorobotics can be combined with more classical robot-based approaches to the study of living systems, leading to a so-called simulation-interactive strategy for the mechanistic explanation of social behaviour in animals.

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