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An embodied agent influences its environment and is influenced by it. We use the sensorimotor loop to model these interactions and quantify the information flows in the system by information-theoretic measures. This includes a measure for the interaction among the agent's body and its environment, often referred to as morphological computation. Additionally, we examine the controller complexity, which can be seen in the context of the integrated information theory of consciousness. Applying this framework to an experimental setting with simulated agents allows us to analyze the interaction between an agent and its environment, as well as the complexity of its controller. Previous research revealed that a morphology adapted well to a task can substantially reduce the required complexity of the controller. In this work, we observe that the agents first have to understand the relevant dynamics of the environment to interact well with their surroundings. Hence an increased controller complexity can facilitate a better interaction between an agent's body and its environment. [ABSTRACT FROM AUTHOR]

Engineers frequently aim to streamline environmental factors to facilitate the effective operation of robots. However, in nature, environmental considerations play a crucial role in shaping the embodiment of organisms. To comply robots with the complexity of real‐world environments, embedding similar intelligence is key. In the field of soft robotics, various approaches offer insight into how intelligence can be integrated into artificial agents. A discussed topic is the intricate relationship between the brain and the body at the core of intelligence in robots. The goal of this article is, therefore, to unravel the strategies to implement different types of intelligence currently adopted in soft robots. A classification is made by making a distinction between agents that adapt to their environment by 1) their adaptive shape, 2) their adaptive functionality, and 3) their adaptive mechanics. Additionally, the perspectives on intelligence based on their computational approach are distinguished: centralized computation, decentralized computation, or embedded computation. It is concluded that a tailored robotic design approach attuned to specific environmental demands is needed. To unlock the full potential of soft robots, a fresh perspective on embodied intelligence is described, so‐called mechanical intelligence, emphasizing the robot's responsiveness to changing external conditions of a real‐world environment.

Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control. [ABSTRACT FROM AUTHOR]

One feature of animal wings is their embedded mechanosensory system that can support flight control. Insect wings are particularly interesting as they are highly deformable yet the actuation is limited to the wing base. It is established that strain sensors on insect wings can directly mediate reflexive control; however, little is known about airflow sensing by insect wings. What information can flow sensors capture and how can flow sensing benefit flight control? Here, we use the dragonfly (Sympetrum striolatum) as a model to explore the function of wing sensory bristles in the context of flight control. Combining our detailed anatomical reconstructions of both the sensor microstructures and wing architecture, we used computational fluid dynamics simulations to ask the following questions. (1) Are there strategic locations on wings that sample flow for estimating aerodynamically relevant parameters such as the local effective angle of attack? (2) Is the sensory bristle distribution on dragonfly wings optimal for flow sensing? (3) What is the aerodynamic effect of microstructures found near the sensory bristles on dragonfly wings? We discuss the benefits of flow sensing for flexible wings and how the evolved sensor placement affects information encoding. [ABSTRACT FROM AUTHOR]

Biological undulation enables legless creatures to move naturally, and robustly in various environments. Consequently, many kinds of undulating robots have been developed. However, the fundamental mechanism of biological undulation gait generation has not yet been well explained, which hinders deepening the investigation and optimization of these robots. Towards developing a theory for explaining this biological behavior, which will further guide the design of artificial undulation systems, we propose a hypothesis based on both biological findings and previous robotics studies. To verify the hypothesis, we investigate embodied intelligence of undulation locomotion via a mechanical system. Through experimental study, we observe the phenomenon that undulation gait is a production of the source, which is the torque inputs, and the filter, which is the natural dynamics of the system. We further derive a general mathematical model and conduct morphological computation accordingly. From a simple model to a complicated system, our work explores the principles of undulation gait generation. Our findings significantly simplify the control system design of artificial undulating systems.

We describe non-equilibrium ϕ4 theory in a hierarchical manner to develop a method for manipulating coherent fields as a toy model of introducing control into Quantum Field Theory (QFT) of the brain, which is called Quantum Brain Dynamics (QBD). We begin with the Lagrangian density of ϕ4 model, where we adopt 2-Particle-Irreducible (2PI) effective action, and derive the Klein–Gordon equation of coherent fields with a damping term as an input–output equation proposed in areas of morphological computation or reservoir computing. Our analysis is extended to QFT in a hierarchy representing multiple layers covering cortex in a brain. We find that the desired target function is achieved via time-evolution in the Klein–Gordon equations in a hierarchy of numerical simulations when a signal in both the input and output prevails over noise in the intermediate layers. Our approach will be applied to control coherent fields in the systems (in a hierarchy) described in the QFT framework, with potential applications allowing the manipulation of quantum fields, especially holograms in QBD. We could then provide realistic physical degrees of freedom of a light–matter system in the contexts of quantum cognition and the associated free-energy principle.

In the paper, we are going to show how robotics is undergoing a shift in a bionic direction after a period of emphasis on artificial intelligence and increasing computational efficiency, which included isolation and extreme specialization. We assemble these new developments under the label of the morphological paradigm. The change in its paradigms and the development of alternatives to the principles that dominated robotics for a long time contains a more general epistemological significance. The role of body, material, environment, interaction and the paradigmatic status of biological and evolutionary systems for the principles of control are crucial here. Our focus will be on the introduction of the morphological paradigm in a new type of robotics and to contrast the interests behind this development with the interests shaping former models. The article aims to give a clear account of the changes in principles of orientation and control as well as concluding general observation in terms of historical epistemology, suggesting further political-epistemological analysis. [ABSTRACT FROM AUTHOR]

In the quest to develop large-area soft sensors, we can look to nature for many examples. Spiderwebs show many fascinating properties that we can seek to understand and replicate in order to develop large-area, soft, and deformable sensing structures. Spiders' webs are used not only to capture prey, but also to localize their prey through the vibrations that they feel through their legs. Inspired by spiderwebs, we developed a large-area tactile sensor for localizing contact points through vibration sensing. We hypothesize that the structure of a web can be leveraged to amplify, filter, or otherwise morphologically tune vibrations to improve sensing capabilities. To explore this design space, we created a means of computationally designing and 3D printing web structures. By using vibration sensors mounted on the edges of webs to simulate a spider monitoring vibrations, we show how varying the structural properties affects the localization performance when using vibration sensors and long short-term memory (LSTM)-based neural network classifiers. We seek to explain the classification performance seen in different webs by considering various metrics of information content for different webs and, hence, provide insight into how bio-inspired spiderwebs can be used to assist large-area sensing structures. [ABSTRACT FROM AUTHOR]

We describe non-equilibrium ϕ 4 theory in a hierarchical manner to develop a method for manipulating coherent fields as a toy model of introducing control into Quantum Field Theory (QFT) of the brain, which is called Quantum Brain Dynamics (QBD). We begin with the Lagrangian density of ϕ 4 model, where we adopt 2-Particle-Irreducible (2PI) effective action, and derive the Klein–Gordon equation of coherent fields with a damping term as an input–output equation proposed in areas of morphological computation or reservoir computing. Our analysis is extended to QFT in a hierarchy representing multiple layers covering cortex in a brain. We find that the desired target function is achieved via time-evolution in the Klein–Gordon equations in a hierarchy of numerical simulations when a signal in both the input and output prevails over noise in the intermediate layers. Our approach will be applied to control coherent fields in the systems (in a hierarchy) described in the QFT framework, with potential applications allowing the manipulation of quantum fields, especially holograms in QBD. We could then provide realistic physical degrees of freedom of a light–matter system in the contexts of quantum cognition and the associated free-energy principle. [ABSTRACT FROM AUTHOR]

Nature is featured by the resiliency, which enables adaptivity to sudden change under many circumstances. Meanwhile, the resiliency in robotic systems is far from comparable to that of the nature. If a robot is partially damaged, often the whole system fails to operate properly. While some approaches have been proposed, the majority of them are focusing on updating the control policy. Such approach, while rather complex, is not always applicable to mechanical damage of the robot body, especially parts that continuously interact with the surrounding environment. In the previous works Nguyen and Ho, (2022) and Nguyen and Ho, (2021) we introduced an artificial whiskered sensor that exhibited resilience against physical damage by active change of its morphology around the placement of sensory elements (strain gauges), which allowed compensation of location sensing when the whisker was trimmed. In this paper, we extend the approach by using the whisker sensor for texture discrimination tasks. We demonstrate that changing the morphology of the whisker again helps to reduce mismatching between prior knowledge in the frequency/time domain of the sensory signal. This allows the sensory whisker to recover the tactile perception on texture discrimination after the whisker is partially damaged. Furthermore, we also observe that using adaptive sensor morphology would augment tactile perception without the need of computationally expensive recognition and re-classification. This work is expected to shed a light on a new generation of robots that automatically work in the open world where self-maintenance against uncertainties is needed.

Mougenot and Matheson (2024) make a compelling case for the development of a mechanistic cognitive neuroscience that is embodied. However, their analysis of extant work under this header plays down important distinctions between 'minimal' and 'radical' embodiment. The former remains firmly neurocentric and therefore has limited potential to move the needle in understanding the functional contributions of neural dynamics to cognition in the context of wider organism-environment dynamics. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of adding an upper body to a bipedal robot on its stable walking behavior. The robot's parts are mutually interconnected through an actuator network system. Therefore, the movement pattern of the upper body depends on the type of interactions created with other limbs. Throughout the experiments, various interactions among the different body parts were tested. The results showed that a robot with a motionless upper body exhibited unstable walking behavior. However, once the same upper body was involved and interacted properly, with other body parts, its movement significantly helped to stabilize the behavior of the robot. [ABSTRACT FROM AUTHOR]

The mechanical properties of a sensor strongly affect its tactile sensing capabilities. By exploiting tactile filters, mechanical structures between the sensing unit and the environment, it is possible to tune the interaction dynamics with the surrounding environment. But how can we design a good tactile filter? Previously, the role of filters’ geometry and stiffness on the quality of the tactile data has been the subject of several studies, both implementing static filters and adaptable filters. State-of-the-art works on online adaptive stiffness highlight a crucial role of the filters’ mechanical behavior in the structure of the recorded tactile data. However, the relationship between the filter’s and the environment’s characteristics is still largely unknown. We want to show the effect of the environment’s mechanical properties on the structure of the acquired tactile data and the performance of a classification task while testing a wide range of static tactile filters. Moreover, we fabricated the filters using four materials commonly exploited in soft robotics, to merge the gap between tactile sensing and robotic applications. We collected data from the interaction with a standard set of twelve objects of different materials, shapes, and textures, and we analyzed the effect of the filter’s material on the structure of such data and the performance of nine common machine learning classifiers, both considering the overall test set and the three individual subsets made by all objects of the same material. We showed that depending on the material of the test objects, there is a drastic change in the performance of the four tested filters, and that the filter that matches the mechanical properties of the environment always outperforms the others.

In order to approach the performance of biological locomotion in legged robots, better integration between body design and control is required. In that respect, understanding the mechanics and control of human locomotion will help us build legged robots with comparable efficient performance. From another perspective, developing bioinspired robots can also improve our understanding of human locomotion. In this work, we create a bioinspired robot with a blended physical and virtual impedance control to configure the robot’s mechatronic setup. We consider human neural control and musculoskeletal system a blueprint for a hopping robot. The hybrid electric-pneumatic actuator (EPA) presents an artificial copy of this biological system to implement the blended control. By defining efficacy as a metric that encompasses both performance and efficiency, we demonstrate that incorporating a simple force-based control besides constant pressure pneumatic artificial muscles (PAM) alone can increase the efficiency up to 21% in simulations and 7% in experiments with the 2-segmented EPA-hopper robot. Also, we show that with proper adjustment of the force-based controller and the PAMs, efficacy can be further increased to 41%. Finally, experimental results with the 3-segmented EPA-hopper robot and comparisons with human hopping confirm the extendability of the proposed methods to more complex robots. [ABSTRACT FROM AUTHOR]

Active systems of self-propelled agents, e.g., birds, fish, and bacteria, can organize their collective motion into myriad autonomous behaviors. Ubiquitous in nature and across length scales, such phenomena are also amenable to artificial settings, e.g., where brainless self-propelled robots orchestrate their movements into spatial-temporal patterns via the application of external cues or when confined within flexible boundaries. Like their natural counterparts, these approaches typically require many units to initiate collective motion, so controlling the ensuing dynamics is challenging. Here, we demonstrate a simple mechanism that leverages nonlinear elasticity to tame near-diffusive motile particles in forming structures capable of directed motion and other emergent behaviors. Our elasto-active system comprises two centimeter-sized self-propelled microbots connected with elastic beams. These microbots exert forces that suffice to buckle the beam and set the structure in motion. We first rationalize the physics of the interaction between the beam and the microbots. Then we use reduced-order models to predict the interactions of our elasto-active structures with boundaries, e.g., walls and constrictions, and demonstrate how they can exhibit remarkable emergent behaviors such as maze navigation. These findings demonstrate that allowing and understanding changes in body morphology can enhance the capabilities of active matter systems and enable the design of robotic materials capable of space exploration, adaptation, and complex interactions with their surrounding environment., Competing Interests: Competing interests statement:The authors declare no competing interest.

Robot foot and gripper structures with compliancy using different mechanical solutions have been developed to enhance proper contact formations and gripping on various substrates. The Fin Ray structure is one of the solutions. Although the Fin Ray effect has been proposed and exploited, no detailed investigation has been conducted on the effect of different crossbeam angles inside its frame. Thus, herein, an integrative approach is used, combining 3D printing with soft material, finite element modeling, and neural control to 1) manufacture the Fin Ray structure with compliancy; 2) investigate the effect of different crossbeam angles under different loads and cylindrical substrates; and 3) finally apply it as an efficient compliant robot foot structure for energy‐efficient on‐pipe locomotion. Considering the factors of a large contact area, high energy efficiency, and better durability, the Fin Ray model with nonstandard 10°‐inclined crossbeams provides the best compromise in comparison with other models, within the constraints of the defined geometric parameters.

Robot foot and gripper structures with compliancy using different mechanical solutions have been developed to enhance proper contact formations and gripping on various substrates. The Fin Ray structure is one of the solutions. Although the Fin Ray effect has been proposed and exploited, no detailed investigation has been conducted on the effect of different crossbeam angles inside its frame. Thus, herein, an integrative approach is used, combining 3D printing with soft material, finite element modeling, and neural control to 1) manufacture the Fin Ray structure with compliancy; 2) investigate the effect of different crossbeam angles under different loads and cylindrical substrates; and 3) finally apply it as an efficient compliant robot foot structure for energy‐efficient on‐pipe locomotion. Considering the factors of a large contact area, high energy efficiency, and better durability, the Fin Ray model with nonstandard 10°‐inclined crossbeams provides the best compromise in comparison with other models, within the constraints of the defined geometric parameters. [ABSTRACT FROM AUTHOR]

The Integrated Information Theory provides a quantitative approach to consciousness and can be applied to neural networks. An embodied agent controlled by such a network influences and is being influenced by its environment. This involves, on the one hand, morphological computation within goal directed action and, on the other hand, integrated information within the controller, the agent's brain. In this article, we combine different methods in order to examine the information flows among and within the body, the brain and the environment of an agent. This allows us to relate various information flows to each other. We test this framework in a simple experimental setup. There, we calculate the optimal policy for goal-directed behavior based on the “planning as inference” method, in which the information-geometric em-algorithm is used to optimize the likelihood of the goal. Morphological computation and integrated information are then calculated with respect to the optimal policies. Comparing the dynamics of these measures under changing morphological circumstances highlights the antagonistic relationship between these two concepts. The more morphological computation is involved, the less information integration within the brain is required. In order to determine the influence of the brain on the behavior of the agent it is necessary to additionally measure the information flow to and from the brain.

The soft robotics community is currently wondering what the future of soft robotics is. Therefore, it is very important to identify the directions in which the community should focus its efforts to consolidate its impact. The identification of convincing applications is a priority, especially to demonstrate that some achievements already represent an attractive alternative to current technological approaches in specific scenarios. However, most of the added value of soft robotics has been only theoretically grasped. Embodied Intelligence, being of these theoretical principles, represents an interesting approach to fully exploit soft robotic’s potential, but a pragmatic application of this theory still remains difficult and very limited. A different design approach could be beneficial, i.e., the integration of a certain degree of continuous adaptability in the hardware functionalities of the robot, namely, a “flexible” design enabled by hardware components able to fulfill multiple functionalities. In this paper this concept of flexible design is introduced along with its main technological and theoretical basic elements. The potential of the approach is demonstrated through a biological comparison and the feasibility is supported by practical examples with state-of-the-art technologies.

The Integrated Information Theory provides a quantitative approach to consciousness and can be applied to neural networks. An embodied agent controlled by such a network influences and is being influenced by its environment. This involves, on the one hand, morphological computation within goal directed action and, on the other hand, integrated information within the controller, the agent's brain. In this article, we combine different methods in order to examine the information flows among and within the body, the brain and the environment of an agent. This allows us to relate various information flows to each other. We test this framework in a simple experimental setup. There, we calculate the optimal policy for goal-directed behavior based on the "planning as inference" method, in which the information-geometric em-algorithm is used to optimize the likelihood of the goal. Morphological computation and integrated information are then calculated with respect to the optimal policies. Comparing the dynamics of these measures under changing morphological circumstances highlights the antagonistic relationship between these two concepts. The more morphological computation is involved, the less information integration within the brain is required. In order to determine the influence of the brain on the behavior of the agent it is necessary to additionally measure the information flow to and from the brain. [ABSTRACT FROM AUTHOR]

In this article I will address the issue of the embodiment of computing systems from the point of view distinctive of the so-called Unconventional Computation, focusing on the paradigm known as Morphological Computation. As a first step, I will contextualize Morphological Computation within the disciplinary field of Embodied Artificial Intelligence: broadly conceived, Embodied Artificial Intelligence may be characterized as embracing both conventional and unconventional approaches to the artificial emulation of natural intelligence. Morphological Computation stands out from other paradigms of unconventional Embodied Artificial Intelligence in that it discloses a new, closer kind of connection between embodiment and computation. I will further my investigation by briefly reviewing the state-of-the-art in Morphological Computation: attention will be given to a very recent trend, whose core concept is that of "organic reconfigurability". In this direction, as a final step, two advanced cases of study of organic or living morphological computers will be presented and discussed. The prospect is to shed some light on our title question: what progress has been made in understanding the embodiment of computing systems? [ABSTRACT FROM AUTHOR]

Robots are required to be significantly compliant and versatile to work in unstructured environments. In a number of studies, robots have positively exploited the environments during interactions and completed tasks from a morphological viewpoint. Modular robots can help realize real-world adaptive robots. Researchers have been investigating the actuation, coupling, and communication mechanisms among these robots to realize versatility. However, the diverse force transmission among modules needs to be further studied to achieve the adaptive whole-body dynamics of a robot. In this study, we fabricated a modular robot and proposed the realization of force transmission on this robot, by constructing fluid transferable network systems on the actuation modules. By exploiting the physical property variations of the modular robot, our experimental results prove that the robot's motion can be changed by switching the connection pattern of the system. [ABSTRACT FROM AUTHOR]

“Morphological computation” is an increasingly important concept in robotics, artificial intelligence, and philosophy of the mind. It is used to understand how the body contributes to cognition and control of behavior. Its understanding in terms of “offloading” computation from the brain to the body has been criticized as misleading, and it has been suggested that the use of the concept conflates three classes of distinct processes. In fact, these criticisms implicitly hang on accepting a semantic definition of what constitutes computation. Here, I argue that an alternative, mechanistic view on computation offers a significantly different understanding of what morphological computation is. These theoretical considerations are then used to analyze the existing research program in developmental biology, which understands morphogenesis, the process of development of shape in biological systems, as a computational process. This important line of research shows that cognition and intelligence can be found across all scales of life, as the proponents of the basal cognition research program propose. Hence, clarifying the connection between morphological computation and morphogenesis allows for strengthening the role of the former concept in this emerging research field.

Sensor morphology and structure has the ability to significantly aid and improve tactile sensing capabilities, through mechanisms such as improved sensitivity or morphological computation. However, different tactile tasks require different morphologies posing a challenge as to how to best design sensors, and also how to enable sensor morphology to be varied. We introduce a jamming filter which, when placed over a tactile sensor, allows the filter to be shaped and molded online, thus varying the sensor structure. We demonstrate how this is beneficial for sensory tasks analyzing how the change in sensor structure varies the information that is gained using the sensor. Moreover, we show that appropriate morphology can significantly influence discrimination, and observe how the selection of an appropriate filter can increase the object classification accuracy when using standard classifiers by up to 28%.

Eco-cognitive computationalism considers computation in context, following some of the main tenets advanced by the recent cognitive science views on embodied, situated, and distributed cognition. It is in the framework of this eco-cognitive perspective that we can usefully analyze the recent attention in computer science devoted to the importance of the simplification of cognitive and motor tasks caused in organic entities by the morphological features: ignorant bodies can be domesticated to become useful "mimetic bodies", that is able to render an intertwined computation simpler, resorting to that "simplexity" of animal embodied cognition, which represents one of the main quality of organic agents. Through eco-cognitive computationalism we can clearly acknowledge that the concept of computation changes, depending on historical and contextual causes, and we can build an epistemological view that illustrates the "emergence" of new kinds of computations, such as the one regarding morphological computation. This new perspective shows how the computational domestication of ignorant entities can originate new unconventional cognitive embodiments. In the last part of the article I will introduce the concept of overcomputationalism, showing that my proposed framework helps us see the related concepts of pancognitivism, paniformationalism, and pancomputationalism in a more naturalized and prudent perspective, avoiding the excess of old-fashioned ontological or metaphysical overstatements. [ABSTRACT FROM AUTHOR]

Robots still struggle to dynamically traverse complex 3D terrain with many large obstacles, an ability required for many critical applications. Body–obstacle interaction is often inevitable and induces perturbation and uncertainty in motion that challenges closed-form dynamic modeling. Here, inspired by recent discovery of a terradynamic streamlined shape, we studied how two body shapes interacting with obstacles affect turning and pitching motions of an open-loop multi-legged robot and cockroaches during dynamic locomotion. With a common cuboidal body, the robot was attracted towards obstacles, resulting in pitching up and flipping-over. By contrast, with an elliptical body, the robot was repelled by obstacles and readily traversed. The animal displayed qualitatively similar turning and pitching motions induced by these two body shapes. However, unlike the cuboidal robot, the cuboidal animal was capable of escaping obstacle attraction and subsequent high pitching and flipping over, which inspired us to develop an empirical pitch-and-turn strategy for cuboidal robots. Considering the similarity of our self-propelled body–obstacle interaction with part–feeder interaction in robotic part manipulation, we developed a quasi-static potential energy landscape model to explain the dependence of dynamic locomotion on body shape. Our experimental and modeling results also demonstrated that obstacle attraction or repulsion is an inherent property of locomotor body shape and insensitive to obstacle geometry and size. Our study expands the concept and usefulness of terradynamic shapes for passive control of robot locomotion to traverse large obstacles using physical interaction. Our study is also a step in establishing an energy landscape approach to locomotor transitions. [ABSTRACT FROM AUTHOR]

Material-bound vibrations are ubiquitous in the environment and are widely used as an information source by animals, whether they are generated by biotic or abiotic sources. The process of vibration information transfer is subject to a wide range of physical constraints, especially during the vibration transmission phase. This is because vibrations must travel through materials in the environment and body of the animal before reaching embedded mechanosensors. Morphology therefore plays a key and often overlooked role in shaping information flow. Web-building spiders are ideal organisms for studying vibration information transfer due to the level of control they have over morphological traits, both within the web (environment) and body, which can give insights for bioinspired design. Here we investigate the mechanisms governing vibration information transfer, including the relative roles of constraints and control mechanisms. We review the known and theoretical contributions of morphological and behavioral traits to vibration transmission in these spiders, and propose an interdisciplinary framework for considering the effects of these traits from a biomechanical perspective. Whereas morphological traits act as a series of springs, dampers and masses arranged in a specific geometry to influence vibration transmission, behavioral traits influence these morphologies often over small timescales in response to changing conditions. We then explore the relative roles of constraints and control mechanisms in shaping the variation of these traits at various taxonomic levels. This analysis reveals the importance of morphology modification to gain control over vibration transmission to mitigate constraints and essentially promote information transfer. In particular, we hypothesize that morphological computation is used by spiders during vibration information transfer to reduce the amount of processing required by the central nervous system (CNS); a hypothesis that can be tested experimentally in the future. We can take inspiration from how spiders control vibration transmission and apply these insights to bioinspired engineering. In particular, the role of morphological computation for vibration control could open up potential developments for soft robots, which could use multi-scale vibration sensory systems inspired by spiders to quickly and efficiently adapt to changing environments.

Voluntary movements, like point-to-point or oscillatory human arm movements, are generated by the interaction of several structures. High-level neuronal circuits in the brain are responsible for planning and initiating a movement. Spinal circuits incorporate proprioceptive feedback to compensate for deviations from the desired movement. Muscle biochemistry and contraction dynamics generate movement driving forces and provide an immediate physical response to external forces, like a low-level decentralized controller. A simple central neuronal command like “initiate a movement” then recruits all these biological structures and processes leading to complex behavior, e.g., generate a stable oscillatory movement in resonance with an external spring-mass system. It has been discussed that the spinal feedback circuits, the biochemical processes, and the biomechanical muscle dynamics contribute to the movement generation, and, thus, take over some parts of the movement generation and stabilization which would otherwise have to be performed by the high-level controller. This contribution is termed morphological computation and can be quantified with information entropy-based approaches. However, it is unknown whether morphological computation actually differs between these different hierarchical levels of the control system. To investigate this, we simulated point-to-point and oscillatory human arm movements with a neuro-musculoskeletal model. We then quantify morphological computation on the different hierarchy levels. The results show that morphological computation is highest for the most central (highest) level of the modeled control hierarchy, where the movement initiation and timing are encoded. Furthermore, they show that the lowest neuronal control layer, the muscle stimulation input, exploits the morphological computation of the biochemical and biophysical muscle characteristics to generate smooth dynamic movements. This study provides evidence that the system's design in the mechanical as well as in the neurological structure can take over important contributions to control, which would otherwise need to be performed by the higher control levels.

It is hypothesized that the nonlinear muscle characteristic of biomechanical systems simplify control in the sense that the information the nervous system has to process is reduced through off-loading computation to the morphological structure. It has been proposed to quantify the required information with an information-entropy based approach, which evaluates the minimally required information to control a desired movement, i.e., control effort. The key idea is to compare the same movement but generated by different actuators, e.g., muscles and torque actuators, and determine which of the two morphologies requires less information to generate the same movement. In this work, for the first time, we apply this measure to numerical simulations of more complex human movements: point-to-point arm movements and walking. These models consider up to 24 control signals rendering the brute force approach of the previous implementation to search for the minimally required information futile. We therefore propose a novel algorithm based on the pattern search approach specifically designed to solve this constraint optimization problem. We apply this algorithm to numerical models, which include Hill-type muscle-tendon actuation as well as ideal torque sources acting directly on the joints. The controller for the point-to-point movements was obtained by deep reinforcement learning for muscle and torque actuators. Walking was controlled by proprioceptive neural feedback in the muscular system and a PD controller in the torque model. Results show that the neuromuscular models consistently require less information to successfully generate the movement than the torque-driven counterparts. These findings were consistent for all investigated controllers in our experiments, implying that this is a system property, not a controller property. The proposed algorithm to determine the control effort is more efficient than other standard optimization techniques and provided as open source.

Classic control theory applied to compliant and soft robots generally involves an increment of computation that has no equivalent in biology. To tackle this, morphological computation describes a theoretical framework that takes advantage of the computational capabilities of physical bodies. However, concrete applications in robotic locomotion control are still rare. Also, the trade-off between compliance and the capacity of a physical body to facilitate its own control has not been thoroughly studied in a real locomotion task. In this paper, we address these two problems on the state-of-the-art hydraulic robot HyQ. An end-to-end neural network is trained to control HyQ's joints positions and velocities using only Ground Reaction Forces. Our simulations and experiments demonstrate better controllability using less memory and computational resources when increasing compliance. However, we show empirically that this effect cannot be attributed to the ability of the body to perform intrinsic computation. It invites to give an increased emphasis on compliance and co-design of the controller and the robot to facilitate attempts in machine learning locomotion. [ABSTRACT FROM AUTHOR]

Recently, soft robots that consist of soft and deformable materials have received much attention for their adaptability to uncertain environments. Although these robots are difficult to control with a conventional control theory owing to their complex body dynamics, research from different perspectives attempts to actively exploit these body dynamics as an asset rather than a drawback. This approach is called morphological computation, in which the soft materials are used for computation that includes a new kind of control strategy. In this article, we propose a novel approach to analyze the computational properties of soft materials based on an algebraic method, called the input–output equation used in systems analysis, particularly in systems biology. We mainly focus on the two scenarios relevant to soft robotics, that is, analysis of the computational capabilities of soft materials and design of the input force to soft devices to generate the target behaviors. The input–output equation directly describes the relationship between inputs and outputs of a system, and hence by using this equation, important properties, such as the echo state property that guarantees reproducible responses against the same input stream, can be investigated for soft structures. Several application scenarios of our proposed method are demonstrated using typical soft robotic settings in detail, including linear/nonlinear models and hydrogels driven by chemical reactions. [ABSTRACT FROM AUTHOR]

For robots to navigate successfully in the real world, unstructured environment adaptability is a prerequisite. Although this is typically implemented within the control layer, there have been recent proposals of adaptation through a morphing of the body. However, the successful demonstration of this approach has mostly been theoretical and in simulations thus far. In this work we present an underwater hopping robot that features a deformable body implemented as a deployable structure that is covered by a soft skin for which it is possible to manually change the body size without altering any other property (e.g. buoyancy or weight). For such a system, we show that it is possible to induce a stable hopping behavior instead of a fall, by just increasing the body size. We provide a mathematical model that describes the hopping behavior of the robot under the influence of shape-dependent underwater contributions (drag, buoyancy, and added mass) in order to analyze and compare the results obtained. Moreover, we show that for certain conditions, a stable hopping behavior can only be obtained through changing the morphology of the robot as the controller (i.e. actuator) would already be working at maximum capacity. The presented work demonstrates that, through the exploitation of shape-dependent forces, the dynamics of a system can be modified through altering the morphology of the body to induce a desirable behavior and, thus, a morphological change can be an effective alternative to the classic control. [ABSTRACT FROM AUTHOR]

The paper is intended as an exploration of the potential methodology for hypercomputing using the long intellectual experience of the study of morphology and morphogenesis. The first part of the paper shows that the missing element of the process of computation in the studies searching for the generalization of computation is encoding. Turing Machines can work only on information encoded in a very specific way. When we look for ways to generalize computation, the analysis of encoding is the most likely source of ideas for the extension of this concept. The second part of the paper is an exploration of the history of morphogenesis from the perspective of the search for a method of unconventional encoding information.

Eco-cognitive computationalism sees computation in context, adopting the intellectual visions advanced by the cognitive science perspectives on embodied, situated, and distributed cognition. It is in this framework that we can fruitfully study the relevance in recent computer science devoted to the simplification of cognitive and motor tasks generated in organic entities by the morphological aspects. Ignorant bodies can be cognitively “domesticated” to become useful “mimetic bodies'', which originate eccentric new computational embodiments capable of rendering an involved computation simpler and more efficient. On the basis of these considerations, we will also see how the concept of computation changes, being related to historical and contextual factors, so that the “emergence'' of new kinds of computations can be epistemologically clarified, such as the one regarding morphological computation. Finally, my presentation will introduce and discuss the concept of overcomputationalism, as intertwined with the traditional concepts of pancognitivism, paniformationalism, and pancomputationalism, seeing them in a more naturalized intellectual disposition, more appropriate to the aim of bypass ontological or metaphysical overstatements.

The Special Issue contains the selected articles presented at the 15th International Conference on the Simulation of Adaptive Behavior (SAB 2018). The conference took place during August 2018 in Frankfurt, Germany. The articles introduce different aspects of closed-loop dynamic computations for adaptive behavior in artificial agents. The aspects cover a range of adaptive behavior research from morphological computation to brain-body-environment interactions, nature-inspired special perception, and closed-loop online learning. SAB is a biennial conference; its next incarnation will be during September 2020, in Paris. If the papers in this issue inspire you, please consider submitting your work to the 2020 conference—who knows, next time it may be your paper in the Special Issue. [ABSTRACT FROM AUTHOR]

Realizing adaptability to environmental changes requires the robot’s body to change according to the environment. From this perspective, several studies have considered variable compliant actuation to change the physical characteristics of the robot as it interacts with the environment. Robots with this ability show a variety of efficient stable motions during contact with the environment. However, having locally variable compliant parts with independent dynamics under the same body may be insufficient for achieving adaptability in diverse environments. To extend the idea of variable compliant actuation, from being used locally to the level of the entire body’s dynamics, in this study, we use an actuator network system (ANS). As an approximation of bipedal robots, we developed an eight-legged rimless wheel robot with an ANS that allows energy transfer among limbs through passive interactions among mutually interconnected air cylinders mounted on the robot’s legs. We experimentally demonstrated that by using the ANS to realize energy transfer among limbs, the robot performance improved. [ABSTRACT FROM AUTHOR]