Gait emergence and adaptation in animals is unmatched in robotic systems. Animals can create and recover locomotive functions "on-the-fly" after an injury whereas locomotion controllers for robots lack robustness to morphological changes. In this work, we extend previous research on emergent interlimb coordination of legged robots based on coupled phase oscillators with force feedback terms. We investigate how the coupling weights between these phase oscillators can be extracted from the morphology with a fast and computationally lightweight method based on a combination of twitching and Hebbian learning to form sensor-motor maps. The coefficients of these maps create naturally scaled weights, which not only lead to robust gait limit cycles, but can also adapt to morphological modifications such as sensor loss and limb injuries within a few gait cycles. We demonstrate the approach on a robotic quadruped and hexapod.

While most modern-day quadruped robots crouch their limbs during the stance phase to stabilize the trunk, mammals exploit the inverted-pendulum motions of their limbs and realize both efficient and stable walking. Although the flexibility of the shoulder region of mammals is expected to contribute to reconciling the discrepancy between the forelimbs and hindlimbs for natural walking, the complex body structure makes it difficult to understand the functionality of animal morphology. In this study, we developed a simple robot model that mimics the flexibility of shoulder region in the sagittal plane, and we conducted a two-dimensional simulation. The results suggest that the flexibility of the shoulder contributes to absorbing the different motions between the forelimbs and hindlimbs.

A swarm robotic system is a system in which multiple robots cooperate to fulfill a macroscopic function. Many swarm robots have been developed for various purposes. This study aims to design swarm robots capable of executing spatially distributed tasks effectively, which can be potentially used for tasks such as search-and-rescue operation and gathering scattered garbage in rooms. We propose a simple decentralized control scheme for swarm robots by extending our previously proposed non-reciprocal-interaction-based model. Each robot has an internal state, called its workload. Each robot first moves randomly to find a task, and when it does, its workload increases, and then it attracts its neighboring robots to ask for their help. We demonstrate, via simulations, that the proposed control scheme enables the robots to effectively execute multiple tasks in parallel under various environments. Fault tolerance of the proposed system is also demonstrated.

An optional robotic arm unit, like animals' neck, allows quadruped robot to achieve multifunctional tasks such as manipulation and patrol. However, increases in body weight due to the neck unit will make energetic issues in legged robots' locomotion more serious. This study presents a minimal decentralized coordination mechanism between a neck and limbs, especially focusing on quadruped mammals' nodding behaviors for efficient walking. The results of two dimensional (2D) simulation suggest that bilateral sensory feedback mechanism between neck and limbs plays essential roles for the quadruped robot to coordinate neck and limbs in response to the physical situation of the body parts, achieving efficient quadrupedal walking.

Snakes are able to move effectively by using terrain irregularities as scaffolds against which they push their bodies. This locomotion is attractive from a robotic viewpoint because irregularities in the environment of conventional robots interfere with their operation. In a previous work, we proposed a decentralized control mechanism of the scaffold-based locomotion of snakes, which combined curvature derivative control with local pressure reflex. Here, we practically demonstrate how a snake-like robot utilizing the proposed control scheme moves effectively by pushing its body against pegs.

Behavioral diversity is an essential feature of living systems, enabling them to exhibit adaptive behavior in hostile and dynamically changing environments. However, traditional engineering approaches strive to avoid, or suppress, the behavioral diversity in artificial systems to achieve high performance in specific environments for given tasks. The goals of this research include understanding how living systems exhibit behavioral diversity and using these findings to build lifelike robots that exhibit truly adaptive behaviors. To this end, we have focused on one of the most primitive forms of intelligence concerning behavioral diversity, namely, a plasmodium of true slime mold. The plasmodium is a large amoeba-like unicellular organism that does not possess any nervous system or specialized organs. However, it exhibits versatile spatiotemporal oscillatory patterns and switches spontaneously between these. Inspired by the plasmodium, we built a mathematical model that exhibits versatile oscillatory patterns and spontaneously transitions between these patterns. This model demonstrates that, in contrast to coupled nonlinear oscillators with a well-designed complex diffusion network, physically interacting mechanosensory oscillators are capable of generating versatile oscillatory patterns without changing any parameters. Thus, the results are expected to shed new light on the design scheme for lifelike robots that exhibit amazingly versatile and adaptive behaviors.

Autonomous decentralized control is a key concept for the realization of highly adaptive behavior. However, universal design of autonomous decentralized control that ensures rich adaptability is still lacking. In this study, we tackle this problem through the development of a two-dimensional sheet-like robot, SheetBot. The SheetBot is a suitable model system for the establishment of universal design principles for autonomous decentralized control, because it can bend reasonably to the circumstances encountered due to its large surface area, and also because its applications are widely expected. To realize highly adaptive locomotion with SheetBot, we are inspired by the decentralized control scheme of the scaffold-based locomotion of snakes. We extend this design scheme to a two-dimensional bodily structure on the basis of a continuum model. Simulation results show that SheetBot can locomote on various kinds of irregular terrain with minimal control inputs by implementing the proposed autonomous decentralized control scheme.

Versatile gait patterns are observed in quadrupeds according to the locomotion speed, environmental conditions, and animal species. These gait patterns are generated via inter- and intra-limb coordination mechanisms, both of which are controlled in part by an intraspinal neural network called the central pattern generator (CPG). Previous CPG-based models mainly focused on the inter-limb coordination mechanisms and not on the intra-limb coordination mechanisms, although both of them should play a pivotal role in generating various gait patterns. In this study, we present an autonomous decentralized control scheme for quadruped locomotion wherein inter- and intra-limb coordination mechanisms are well coupled. Simulation results show that the quadruped exhibits transitioning between walking and running and the ability to adapt to changes in body properties by appropriately modifying the phase relationship among body points through well-balanced coupling of the inter- and intra-limb coordination mechanisms. We also present a physical robot that we are currently developing.

Autonomous decentralized control is an attractive concept for designing robots that can exhibit highly adaptive animal-like behaviour. Despite its appeal, previous robots could only adapt to a limited number of environments. To tackle this problem, we hypothesize that robots will move in various environments by simply implementing a “scaffold-exploitation mechanism”. In our previous work, we drew our inspiration from flatworms, which move over various terrains using their two-dimensional sheet-like body, and designed an autonomous decentralized control scheme for a similar robot, SheetBot. In this study, we extend our previous control scheme so that it can effectively exploit the features in its surrounding environment as scaffolds. The validity of the extended control scheme is confirmed by simulations. Our control scheme is not specific to a certain environment but could be applicable in any environment; it thus forms the basis for developing a multi-terrestrial robot whose working area covers land, sea, and even air.

In this paper, we investigate an adaptive bipedal walking control that exploits sensory information stemming from “soft deformable” feet. To this end, we developed a bipedal robot with soft deformable feet and proposed an unconventional CPG (central pattern generator)-based control that exploits the local force feedback generated from such deformation. Through experiments with the constructed robot, we have found that the robot exhibits a remarkably adaptive walking ability in response to a change in walking velocity and external perturbations. These results support the conclusion that the “deformation” of a robot's body plays a pivotal role in the emergence of “sensory-motor coordination”, which is the key for generating adaptive locomotion in a robotic system.

Versatile gait patterns that depend on the locomo- tion speed, environmental conditions, and animal species are observed in quadrupeds. Locomotor patterns are generated via the interlimb coordination, which is partially controlled by an intraspinal neural network called the “central pattern generator” (CPG). However, there is currently no clear understanding of the adaptive interlimb coordination mechanism. We hypothesize that the interlimb coordination should rely more on the “physical” interaction between leg movements through the body rather than the interlimb neural connection. To understand the coordination mechanism, we developed a simple-structured quadruped robot and proposed an unconventional CPG model that consists of four decoupled oscillators with only local force feedback in each leg. Experimental results show that our CPG model allows the robot to exhibit steady gait patterns, adaptability to changes in body properties, and adaptive gait transition between walking and trotting. Our robot mimics locomotor patterns of real quadrupeds following which it can capture the basic mechanism underlying the adaptive interlimb coordination.

Snakes actively utilize terrain irregularities and attain propulsion force by pushing their bodies against scaffolds. The objective of this study is to understand the mechanism underlying this locomotion on the basis of a synthetic approach. Our previous control scheme of a snake-like robot, in which the curvature derivative control method is combined with a reflexive mechanism using local pressures on the body wall, could not fully reproduce innate behavior of real snakes. In this study, we improve our previous control scheme. We show that the locomotion of a simulated robot with the improved control scheme is surprisingly in good agreement with the locomotion of a real snake. We also present a physical robot that we are currently developing.

Snakes actively utilize irregularities in terrains and attain propulsion force by pushing their bodies against scaffolds. The objective of this study is to understand the mechanism underlying such functionalities of snakes on the basis of a synthetic approach. We construct a model of a serpentine robot with viscoelastic properties, and we design an autonomous decentralized control scheme that employs local sensory feedback based on the muscle length and strain of the body, the latter of which is generated by the body softness. Simulation results show that the robot exhibits locomotion effectively by utilizing scaffolds such as high-friction areas and pegs, which is in fairly good agreement with biological findings.

This paper presents a two-dimensional simulation model of an amoeboid robot that exhibits locomotion in a decentralized manner. The significant feature to note is that the model does not control friction between parts of the robot and ground explicitly but exploits passive dynamics of the inner fluid of the robot, i.e., thixotropic flow, in order to generate locomotion. Thixotropy is a very interesting rheological property of a fluid to form a gelled structure over time when not subject to shearing and then to liquefy when agitated, which is observed in protoplasmic streaming of amoeba and plasmodium of true slime mold. Simulation results show that embedding this passive dynamics induces morphological positive feedback mechanism, leading to convection of the inner fluid, which in turn generates locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on revealing the secret of how decentralized control should be designed.

The mechanism of multi-articular muscles that contributes to the emergence of macroscopic behavior of animals has not been clarified thus far. To address this issue, we focus on the slithering motion of snakes, which have numerous antagonistic muscle-tendon chains that span several tens of vertebrae. From theoretical analyses based on the continuum model, the acceleration of the body in the longitudinal direction is found to be proportional to the number of segments spanned by each muscle. On the basis of this theoretical result, we propose a decentralized control scheme for the efficient locomotion of a multi-articular snake-like robot. We conduct simulations using this control scheme, and the results show that the locomotion efficiency increases as the number of segments spanned by each muscle increases.

Autonomous decentralized control is a key concept in our understanding of the mechanism underlying locomotion of animals, wherein the phasic pattern of motion and the excitation pattern of muscle tonus are well reconciled. In our recent work, we proposed a decentralized control scheme in which phasic control and tonic control are well coordinated. In this study, we develop a real snake-like robot to validate our proposed control scheme. Experimental results show that the robot exhibits highly adaptive behavior in response to environmental changes. The results obtained are expected to shed new light on the design of autonomous decentralized control systems.

Autonomous decentralized control is a key concept for understanding the mechanism underlying the adaptive and versatile behavior of animals. Although the design methodology of decentralized control based on a dynamical system approach that can impart adaptability by using coupled oscillators has been proposed in previous studies, it cannot reproduce the versatility of animal behaviors comprehensively. Therefore, our objective is to understand behavioral versatility from the perspective of well-coordinated rhythmic and non-rhythmic movements. To this end, we focus on ophiuroids as simple “best” model living organisms that exhibit spontaneous role assignment of rhythmic and non-rhythmic arm movements, and we model such arm movements by using an active rotator model that can describe both oscillatory and excitatory properties. Simulation results show that the spontaneous role assignment of arm movements is successfully realized by using the proposed model, and the simulated locomotion is qualitatively equivalent to the locomotion of real ophiuroids. This fact can potentially facilitate a better understanding of the control mechanism responsible for behavioral versatility in animals.

This is the first study of a real physical kneed bipedal robot that exhibits passive-dynamic running (PDR), i.e., a bipedal gait with a flight phase in a device without an actuator. By carefully designing the properties of the elastic elements implemented into the hip joints and the stance legs in this device, we achieved a stable PDR consisting of 36 steps. The main contribution of this paper is the demonstration of PDR in the real world, which fully exploits the elastic mechanical properties.

This is the first study of a real physical kneed bipedal robot that exhibits passive dynamic running (PDR). Passive dynamic walking (PDW), which has its roots in the pioneering research of McGeer, intrinsically offers not only nonlinear phenomena such as the pull-in effect and period-doubling bifurcation, but also offers an extremely interesting phenomenon that facilitates the engineering of a highly efficient walking robot. In recent years, a wide variety of verification experiments in PDW were performed using actual devices. In contrast, however, very few studies addressed PDR. In the present study, we developed a two-dimensional real physical passive dynamic running biped with knees. The device stands 400 mm tall and weights 4.8 kg. By carefully designing the properties of the elastic elements implemented into the hip joints and the stance legs in the present device, we achieved stable passive dynamic running of 36 steps. The device runs at about 0.83 m/s down a 0.22 rad slope. To the best of our knowledge, this is a first report of such a performance. This result is expected to prove useful not only for designing human-like natural and efficient bipedal robots, but also for understanding the principles underlying bipedal locomotion.

Despite its appealing concept, a systematic way of designing autonomous decentralized control system is still lacking. In order to alleviate this, we have so far proposed a design scheme for local sensory feedback control leading to adaptive behaviors from the entire system, based on a so-called discrepancy function that extracts the discrepancies between body (i.e., mechanical system), brain (i.e., control system) and environments. This paper intensively investigates the validity of this design scheme under the real world constraints by taking a two-dimensional serpentine robot exhibiting undulatory slithering locomotion as a practical example. The experimental results show that the robot exhibits adaptive behavior against environmental changes as well as the robustness against malfunctions of body segments due to the local sensory feedback control. The results obtained are expected to shed a new light on methodology for autonomous decentralized control system.

Animals exhibit astoundingly adaptive and supple locomotion under real world constraints. In order to endow robots with similar capabilities, we must implement large degrees of freedom, equivalent to animals, into the robots' bodies. For taming large degrees of freedom, the concept of autonomous decentralized control plays a pivotal role. However, a systematic way of designing such autonomous decentralized control system is still missing. Aiming at understanding the principles that underlie animals' locomotion, in our early studies, we focused on true slime mold, a primitive living organism, and extracted a decentralized control scheme. In order to validate this control scheme, this paper presents a soft-bodied amoeboid robot inspired by true slime mold. Significant features of this robot are twofold: (1) the robot has truly soft and deformable body stemming from real-time tunable springs and a balloon, the former is used for an outer skin of the body and the latter serves as protoplasm; and (2) a fully decentralized control using coupled oscillators with completely local sensory feedback mechanism is realized by exploiting the long-distance physical interaction between the body parts stemming from both the softness of the body and the law of conservation of protoplasmic mass. Experimental results show that this robot exhibits truly supple locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on design scheme for autonomous decentralized control system.

Despite its appeal, a systematic design of an autonomous decentralized control system is yet to be realized. To bridge this gap, we have so far employed a “back-to-basics” approach inspired by true slime mold, a primitive living creature whose behavior is purely controlled by coupled biochemical oscillators similar to central pattern generators (CPGs). Based on this natural phenomenon, we have successfully developed a design scheme for local sensory feedback control leading to system-wide adaptive behavior. This design scheme is based on a “discrepancy function” that extracts the discrepancies among the mechanical system (i:e:, body), control system (i:e:, brain-nervous system) and the environment. The aim of this study is to intensively investigate the validity of this design scheme by applying it to the control of a quadruped locomotion. Simulation results show that the quadruped robot exhibits remarkably adaptive behavior in response to environmental changes and changes in body properties. Our results shed a new light on design methodologies for CPG-based decentralized control of various types of locomotion.

Decentralized control is a key concept to understand the mechanism of versatile and adaptive locomotion of animals under various environments. However, a systematic design of an autonomous decentralized control system has not been well established, because previous studies mainly focused on the control of phase relationship among body parts, i.e., phasic control, but not on the control of the spatio-temporal dynamics of muscle tonus, i.e., tonic control. In this paper, we propose a decentralized control scheme that enables to reconcile phasic control and tonic control, by taking serpentine locomotion as a practical example. Through modeling and simulations, we show that well-balanced coupling between the phasic and tonic control is crucial for the generation of adaptive and efficient locomotion.

In this paper, we propose an idea which can solve the complexity of the overlapping situation observed in control system of living things. We introduce an another element between controlled object and control law. This newly introduced element is named as Implicit Control Law and decided by interaction of the controlled object, the control law and the field. Furthermore, the Implicit Control Law does not only solve the indivisibility problem but also produces a start point for understanding of realtime environmental adaptation function of living thing with tiny brain. That is, the Implicit Control Law is a core principle of Mobiligence.

Animals exhibit astoundingly adaptive and supple locomotion under real world constraints. In order to endow robots with similar capabilities, we must implement many degrees of freedom, equivalent to animals, into the robots' bodies. For taming many degrees of freedom, the concept of autonomous decentralized control plays a pivotal role. However, a systematic way of designing such autonomous decentralized control system is still missing. Aiming at understanding the principles that underlie animals' locomotion, in our early studies, we focused on plasmodium of true slime mold, a primitive living organism, and extracted a design scheme for autonomous decentralized control system. In order to demonstrate the relevance of this design scheme, this paper presents a soft-bodied fluid-driven amoeboid robot inspired by plasmodium of true slime mold. The significant features of this robot are twofold: (1) the robot has fluidic circuit (i.e., cylinders and nylon tubes filled with fluid) and truly soft and deformable body stemming from real-time tunable springs, the former serves as protoplasm and the latter is used for elastic actuators; and (2) a fully decentralized control using coupled oscillators with completely local sensory feedback mechanism is realized by exploiting the long-distance physical interaction between the body parts stemming from the law of conservation of protoplasmic mass. The experimental results show that this robot exhibits truly supple locomotion without relying on any hierarchical structure. The results obtained are expected to shed new light on design scheme for autonomous decentralized control system.

In this study, we discuss the common stabilization mechanism underlying passive dynamic walking (PDW) and passive dynamic running (PDR), focusing on the feedback structures in analytical Poincare¿ maps. To this end, we have derived linearized analytical Poincare¿ maps for PDW and PDR, and analyzed these stabilities on two models, namely models with elastic elements and with stiff legs. Through our theoretical analysis, we have found an ¿implicit two-delay feedback structure¿, which can be seen as a certain type of two-delay input digital feedback control developed as an artificial control structure in the field of control theory, is an inherent stabilization mechanism in PDR appearing from the model with elastic elements, and two-period and four-period PDW appearing from with stiff legs. This mechanism is the key to adaptive function underlying phase transition phenomenon between PDW and PDR and period-doubling bifurcation phenomenon in PDW. To the best of our knowledge, this has not yet to be addressed and studied so far. Our results shed new light on the common underlying principle of passive dynamic locomotion, including biped PDW and PDR.

This paper discusses experimental verifications of a two-dimensional modular robot called ¿Slimebot¿, consisting of many identical modules, each of which has simple motile functions. We have so far investigated a fully decentralized algorithm able to control the morphology of the modular robot in real-time according to the environment encountered. One of the most significant features of our approach is that we explicitly exploit ¿emergent phenomena¿ stemming from the interplay between control and mechanical systems. In order to verify our proposed control scheme, we have constructed real physical Slimebot. Experiments with 10 modules suggest that this robot exhibits significant abilities, i.e., adaptivity, scalability, and fault tolerance.

One of the most amazing phenomena widely observed in nature is self-assembly; living systems spontaneously form their body structure through the developmental process. While this remarkable phenomenon are not thoroughly understood in biology, the concept of self-assembly becomes undeniably indispensable also in artificial systems as they increase in size and complexity. Based on this consideration, this paper discusses the realization of self-assembly with the use of a multi-robotic system each of which has simple motile function. The main contributions of this paper are twofold: the first concerns a fully decentralized control method derived from the mutual entrainment between coupled nonlinear oscillators; and the second is related to the exploitation of physical interaction between agents stemming from a passive deformation mechanism, which allows an efficient movement of individual agents during the course of self-assembly. Here, form generation by self-assembly is considered as the result of time evolution toward the most dynamically stable state. Owing to this, in principle, the proposed method also satisfies significant ability of self-repair without making any modification to the proposed algorithm. In this paper we validate proposed method by exploiting real physical robotic agents. Experimental results show that stable and spontaneous self-assembly is achieved irrespective of the initial positional relationship between the agents.

Despite its appealing concept, a systematic way of designing autonomous decentralized control system is still lacking. In order to alleviate this, we have so far proposed a design scheme for local sensory feedback control based on so-called discrepancy function that extracts the discrepancies between body (i.e., mechanical system), brain (i.e., control system) and environments. This paper intensively investigates the validity of this design scheme by taking a two-dimensional serpentine robot exhibiting undulatory slithering locomotion as a practical example. Preliminary simulation results derived strongly indicate that our design methodology allows us to endow the robot with highly adaptability and fault tolerance. These results obtained are expected to shed a new light on design methodology for autonomous decentralized control system. Furthermore, together with a validity verification of the simulation results, this paper introduces a real two-dimensional serpentine robot that is currently under development.

The control and mechanical systems of an embodied agent should be tightly coupled so as to emerge useful functionalities such as adaptivity. This indicates that the mechanical system as well as the control system should be responsible for a certain amount of “computation” for generating the behavior. However, there still leaves much to be understood about to what extent “computational offloading” from the control system to the mechanical system should be achieved. In order to effectively consider this, here we particularly focus on a passive dynamic running biped whose behavior is generated purely from its mechanical system, and investigate how the body’s properties influence the resulting behavior. Through the numerical simulations, we have found that two elastic parameters of its body, leg spring constant and hip coil spring constant, play a crucial role, and depending on which various kinds of stable gait patterns are generated. To the best of our knowledge, this has not been explicitly addressed so far. The results obtained are expected to shed a new light on to what extent the mechanical system should be responsible for generating the behavior.

Modular robots are mechatronic systems that can rearrange their connectivity to create new topologies to accomplish diverse tasks. In previous work, we have studied a modular reconfigurable robot (Slimebot) characterized by a spontaneous inter-module connection control mechanism. The modules of Slimebot connect to each other via a functional material which satisfies physical coupling between the ones. Here, we investigate the effect of heterogeneous inter-module coupling strengths on the adaptivity of Slimebot (here measured in terms of structural stability and locomotive speed). Simulation results show that a certain amount of heterogeneity improves the adaptivity of the system compared to the case of homogeneous modules. The only assumption that needs to be satisfied by the system with heterogeneous couplings is compliance to Steinberg’s energy minimization theory.

The control and mechanical systems of an embodied agent should be tightly coupled so as to emerge useful functionalities such as adaptivity. This indicates that the mechanical system as well as the control system should be responsible for a certain amount of computation for generating the behavior. However, there still leaves much to be understood about how such “computational offloading” from the control system to the mechanical system can be achieved. In order to intensively investigate this, here we particularly focus on the “softness” of the body, and show how the computational offloading derived from this property is exploited to simplify the control system and to increase the degree of adaptivity. To this end, we employ a two-dimensional amoeboid robot as a practical example, consisting of incompressive fluid (i.e. protoplasm) covered with an outer skin composed of a network of real-time tunable springs. Preliminary simulation results show that the exploitation of the “long-distant interaction” stemming from “the law of conservation of protoplasmic mass” allows us to simplify the control mechanism; and that adaptive amoeboid locomotion can be realized without the need of a central controller. The results obtained are expected to shed light on how control and mechanical systems should be coupled, and what the “brain-body-interaction” carefully designed brings to the resulting behavior.

This study is intended to deal with the interplay between control and mechanical systems, and to discuss the "brain-body interaction as it should be" particularly from the viewpoint of learning. To this end, we have employed a decentralized control of a two-dimensional serpentine robot consisting of several identical body segments as a practical example. The preliminary simulation results derived indicate that the convergence of decentralized learning of locomotion control can be significantly improved even with an extremely simple learning algorithm, i.e., a gradient method, by introducing biarticular muscles compared to the one only with monoarticular muscles. This strongly suggests the fact that a certain amount of computation should be off loaded from the brain into its body, which allows robots to emerge various interesting functionalities.

One of the most amazing phenomena widely observed in nature is self-assembly; living systems spontaneously form their body structure through the developmental process. While this remarkable phenomenon are not thoroughly understood in biology, the concept of self-assembly becomes undeniably indispensable also in artificial systems as they increase in size and complexity. Based on this consideration, this paper discusses the realization of self-assembly with the use of a multi-robotic system each of which has simple motile function. The main contributions of this paper are twofold: the first concerns a fully decentralized control method derived from the mutual entrainment between coupled nonlinear oscillators; and the second is related to the exploitation of inter-modular physical interaction stemming from a passive deformation mechanism, which allows an efficient movement of individual modules during the course of self-assembly. Here, form generation by self-assembly is considered as the result of time evolution toward the most dynamically stable state. Owing to this, in principle, the proposed method also satisfies significant ability of self-repair without making any modification to the proposed algorithm. Simulation results show that stable and spontaneous self-assembly is achieved irrespective of the initial positional relationship between the modules.

One of the most graceful phenomena widely observed in nature is self-assembly; living systems spontaneously form their body structure through the developmental process. While this remarkable phenomenon still leaves much to be understood in biology, the concept of self-assembly becomes undeniably indispensable also in artificial systems as they increase in size and complexity. Based on this consideration, this paper discusses the realization of self-assembly with the use of a modular robot. The main contributions of this paper are two-fold: the first concerns the exploitation of emergent phenomena stemming from the interplay between the control and mechanical systems; the second is related to the implementation of the different adhesiveness among the modules. Here, form generation by self-assembly is considered as the result of time evolution toward the most dynamically stable state. Owing to this, the proposed method also satisfies significant ability of self-repair without making any modification to the proposed algorithm. Simulation results show that stable and spontaneous self-assembly is achieved irrespective of the initial positional relationship among the modules

Recently, it has been widely recognized that control and mechanical systems cannot be designed separately due to their tight interdependency. However, there still leaves much to be understood about how well-balanced coupling between control and mechanical systems can be achieved. Therefore, as an initial step toward this goal, this study intensively discusses the effect of the intrinsic dynamics of a robot's body on the resulting behavior, in the hope that mechanical systems appropriately designed will allow us to significantly reduce the complexity of control algorithm required. More precisely, we focus on the property of leg elasticity of a passive dynamic running biped, and investigate how this influences the stability of running. As a result, we have found that a certain type of nonlinearity in the leg elasticity plays a crucial role to enhance the stability of passive dynamic running. To the best of our knowledge, this has never been explicitly considered so far

This paper discusses experimental verifications of a two-dimensional modular robot called "Slimebot", consisting of many identical modules. The Slimebot exhibits adaptive reconfiguration by exploiting a fully decentralized algorithm able to control its morphology according to the environment encountered. One of the significant features of our approach is that we explicitly exploit "emergent phenomena" stemming from the interplay between control and mechanical systems in order to control the morphology in real time. To this end, we particularly focus on a "functional material" and a "mutual entrainment" among nonlinear oscillators, the former of which is used as a spontaneous connectivity control mechanism between the modules, and the latter of which acts as the core of the control mechanism for the generation of locomotion. Experimental results indicate that the proposed algorithm can induce locomotion, which allows us to successfully control the morphology of the modular robot in real time according to the situation without losing the coherence of the entire system

Traditionally, robot control has been done typically by "highly precise control algorithms": the position of each movable body part is accurately determined at any time with vast amount of computation. This, however, causes serious problems, particularly in terms of adaptability and energy efficiency. On the other hand, an extreme approach has been gaining a lot of attention recently. A good instantiation is the passive dynamic walker, driven only by exploiting the intrinsic dynamics of its mechanical system. However, the mechanical system is not everything, just as the control system is not everything; "well-balanced" coupling between control and mechanical systems should be considered. In addition, the "meeting point" between the two systems should be flexibly varied according to the environment encountered. In light of these facts, this study intensively focuses on the stiffness of robots' joints, since this effectively influences the dominance relationship between control and mechanical systems. More specifically, the aim of this study is to develop a "real-time tunable spring" that can smoothly change its elasticity without changing its natural length, allowing robot's joints to change their position and stiffness independently

This paper discusses a fully decentralized algorithm able to control the morphology of a two-dimensional modular robot called “Slimebot”, consisting of many identical modules, according to the environment encountered. One of the significant features of our approach is that we explicitly exploit “emergent phenomena” stemming from the interplay between control and mechanical systems in order to control the morphology in real time. To this end, we particularly focus on a “functional material” and a “mutual entrainment”, the former of which is used as a spontaneous connectivity control mechanism between the modules, and the latter of which plays as the core of the control mechanism for the generation of locomotion. Simulation results indicate that the proposed algorithm can induce “protoplasmic streaming”, which allows us to successfully control the morphology of the modular robot in real time according to the situation without losing the coherence of the entire system.

This paper discusses a fully decentralized algorithm able to control the morphology of a two-dimensional modular robot called "Slimebot", consisting of many identical modules, according to the environment encountered. One of the significant features of our approach is that we explicitly exploit "emergent phenomena" stemming from the interplay between control and mechanical systems in order to control the morphology in real time. To this end, we particularly focus on a "functional material" and a "mutual entrainment", the former of which is used as a spontaneous connectivity control mechanism between the modules, and the latter of which plays as the core of the control mechanism for the generation of locomotion. Simulation results indicate that the proposed algorithm can induce locomotion, which allows us to successfully control the morphology of the modular robot in real time according to the situation without losing the coherence of the entire system.

This paper discusses a fully decentralized algorithm able to control the morphology of a modular robot, consisting of many identical modules, according to the environment encountered. One of the significant features of our approach is that we explicitly exploit "emergent phenomena" stemming from the interaction between control and mechanical dynamics in order to control the morphology in real time. To this end, we particularly focus on a "functional material" and a "mutual entrainment", the former of which is used as a connection mechanism between the modules, and the latter of which plays as the core of the control mechanism for the generation of locomotion. Simulation results indicate that the proposed algorithm can induce "protoplasmic streaming", which allows us to successfully control the morphology of modular robot in real time according to the situation without losing the coherence of the entire system.

This is a review paper on Mobiligence research. In order to solve the mechanism of intelligence with which higher organisms can behave even in unknown environment, and to discover design principle of artificial systems that have such intelligence, a new concept called Mobiligence is introduced based on assumption that the intelligence must be emerged through the interaction of the system with the environment caused by its mobility. The research on the Mobiligence is characterized by embodied plasticity, co-embodiment with environment, and internal generation of abductive information. The framework of the research on Mobiligence is presented in this paper as well as the originality of the research.

Over the past decade, it has been widely recognized that the emergence of intelligence is strongly influenced by not only control systems but also their embodiments, that is the physical properties of robots' bodies. This implies that the control dynamics and its body dynamics cannot be designed "separately" due to their tight interdependency, which is significantly different from the traditional design approach on a "hardware first, software last" basis. Now, some questions arise: how should these two dynamics be coupled? What sort of phenomena will emerge under the so-called "well-balanced" design? In spite of its importance, to our knowledge, still very few studies have explicitly investigated this mutual interaction. In light of these facts, this study is intended to deal with the interaction dynamics between control and mechanical systems, and to analytically and synthetically discuss the "relationship as it should be" between the two dynamics. To this end, a decentralized control for a multi-legged robot is employed as a practical example. The result derived indicates that the convergence of decentralized gait control can be significantly ameliorated by modifying its interaction dynamics between the control and mechanical systems to be implemented.

This paper discusses a fully decentralized algorithm able to create a coherent swarm of autonomous mobile robots from the viewpoint of "computational physics". To this end, we particularly focus on the "molecular dynamics method" and the "stokesian dynamics method", both of which are widely used to investigate multi-body systems. To verify the feasibility of our approach, this idea has been implemented to a swarm of 2D radio-connected autonomous mobile robots as a practical example. Simulation results indicate that the proposed algorithm can control the shape of the swarm appropriately according to the current situation without losing the coherence of the swarm nor exchanging any global information among the modules. Furthermore, we have found that the local interaction that is used to exploit stokesian dynamics plays an essential role in order to maintain the coherence of the swarm particularly under an unstructured environment.

In order to construct truly autonomous mobile robots, the concept of implementability is highly indispensable: all parts such as controllers, power systems, batteries should be embedded inside a body. Therefore, the implementing controller on hardware is one of the most promising ways, since this contributes to low power consumption and miniaturization and so on. Another crucial requirement in the field of autonomous mobile robots is robustness. That is, autonomous mobile robots have to cope with their unpredictably changing environment in real time. In this study, to meet these requirements the concept of dynamically rearrangeable electrical circuit (DREC) is proposed and we implement this onto FPGAs as physical electronic circuits by borrowing the idea from neuromodulation widely observed in biological nervous systems by the diffusion-reaction mechanism of neuromodulators. We developed the DREC for the peg-pushing task as a practical example. We confirmed that the physical DREC can successfully regulate the behavior according to the situation by changing its properties in real time.

In order to create agile locomotion for legged robots, so far various methods have been proposed with the concept of neural circuits, or so-called Central Pattern Generators (CPG). In contrast to these approaches in which monolithic CPG circuits are employed to control locomotion, in this study a polymorphic neural circuit is employed instead, allowing the dynamic change of its properties according to the current situation in real time. To this end, the concept of neuromodulation has been introduced. To verify the feasibility of this approach, this concept has been applied to the control of a 3-D quadruped robot. Simulations have been carried out and the results have showed that the proposed method appropriately changes the walking pattern according to the current situation in real time.

Over the past decade it has been widely recognized that the emergence of intelligence is influenced significantly by not only the controllers implemented but also their embodiment, that is robots' body structure. In spite of its importance, still few studies have explicitly investigated this mutual interaction. In light of these facts, in this paper, the interaction between controllers and their embodiments is investigated. To do so, a synthetic approach is employed. More specifically, an embodiment performing a passive-dynamic-walking function is taken as a practical example, and its locomotion controller that can walk on flat terrain is evolved using a genetic algorithm. Simulations have been carried out and the results have showed that passive-dynamic-walking embodiments provide significantly high evolvability. This strongly suggests that "appropriate" embodiments allow us to increase the efficiency of developing their controllers.

Recently, the evolutionary robotics (ER) approach has attracted attention in the field of robotics and artificial life, since it can automatically synthesize controllers by taking the embodiment and the interaction dynamics with the environment into account. However, the ER approach still has several issues to be solved. One is that it becomes significantly difficult to evolve the whole controller in one go (1-shot evolution), as task complexity and/or environment interaction dynamics increases. This is called the bootstrap problem. It is necessary to develop a method that can efficiently increase a controllers' ability without impairing the previously obtained ones. We propose a method for incremental evolution of neurocontrollers by introducing a receptor-ligand concept, the polymorphic property of neural circuits through diffusion-reaction mechanism of chemical substances, so-called neuromodulators. To investigate the effectiveness of our approach, we take a peg-pushing task, which requires an appropriate sequence of behavior to accomplish a task, as a practical example.

As highly complicated interaction dynamics exist, it is therefore extremely difficult to design controllers for legged robots. Evolutionary robotics is one of the most promising approaches, but there still exist several problems that have to be solved. One of the critical problems is that evolved agents generally tend to over adapt to their given environments through the evolutionary process. In other words, they lack rich adaptability. Therefore, it is highly necessary to establish a method that enables one to efficiently construct adaptive controllers that can cope with different situations. For this purpose we introduce the concept of neuromodulators, allowing the evolvement of neural networks which can adjust not only the synaptic weights, but also the structure of the neural network by blocking and/or activating synapses or neurons. We apply this concept to create an adaptive controller for a quadruped robot which not only can walk forward but also regulate the torque output applied to each joint, according to the current situation.

Proposes an architecture of a neuro-hardware that can be realized on a far smaller-scaled circuit compared to the conventional approach. In order to reduce the scale of the circuits, the architecture employs a method of computing the membrane potential and the sigmoidal function by encapsulating the probability properties into the relative delay between two pulses of different signal lines. The proposed architecture enables us to integrate more than one hundred of neurons on a latest FPGA chip, which means thirteen-fold miniaturization compared to conventional architecture.

The evolutionary robotics approach has been attracting a lot of attention in the field of robotics and artificial life. In this approach, neural networks are widely used to construct controllers for autonomous mobile agents, since they intrinsically have generalization, noise-tolerant abilities and so on. However, there are still open questions: (1) the gap between simulated and real environments, (2) the evolutionary and learning phase are completely separated, and (3) the conflict between stability and evolvability/adaptability. In this paper, we try to overcome these problems by incorporating the concept of dynamic rearrangement function of biological neural networks with the use of neuromodulators. Simulation results show that the proposed approach is highly promising.

Due to the drastic increase of complexity and scale, conventional centralized control methodology has being encountering its limit. To overcome this problem, recently autonomous decentralized control methodology has been attracting a lot of concerns in variety of fields. Biological system is a remarkable instantiation of autonomous decentralized systems and has been expected to provide various fruitful ideas to engineering fields. Among these systems, in this study we particularly focus on the immune system since it has a lot of interesting features such as regulation mechanism of antibody immunological tolerance, metadynamics function and so on. We propose a new adaptation method for decentralized control systems by paying close attention to "moderationism" in the immune system. Finally we will show the possibility of our approach by taking a gait acquisition problem of a hexapod walking robot as a practical example.

In recent years much attention has been focused on behavior-based artificial intelligence (AI), which has already demonstrated its robustness and flexibility against dynamically changing world. However, in this approach, the followings have not yet been resolved: how do we construct an appropriate arbitration mechanism, and how do we prepare appropriate competence modules. In this paper, to overcome these problems, we propose a new decentralised consensus-making system inspired by the biological immune system. And we apply our proposed method to behavior arbitration for an autonomous mobile robot, namely garbage collecting problem that takes into account the concept of self-sufficiency. To verify the feasibility of our method, we carry out some simulations. In addition, we investigate two types of adaptation mechanisms, and try to evolve the proposed artificial immune network using reinforcement signals.

Conventional artificial intelligent (AI) systems have been criticized for their brittleness under hostile/dynamic changing environments. Therefore, much attention has been focused on the reactive planning systems such as behavior-based AI. However, in the behavior-based AI approaches, how to construct a mechanism that realizes adequate arbitration among competence modules is still on open question. In this paper, we propose a new decentralized consensus-making system inspired from the biological immune system. And we apply our proposed method to a behavior arbitration of an autonomous mobile robot as a practical example. To verify the feasibility of our method, we carry out some simulations. In addition, we propose an adaptation mechanism that can be used to construct a suitable immune network for adequate action selection.

Conventional AI technology has been criticized on many grounds such as brittleness under dynamically changing environments. To overcome this problem, other approaches called new AI, emergent computation, animat approach, behavior-based AI and so on were proposed and confirmed their usefulness. On the other hand, biological information processing systems have many interesting functions and are expected to provide various feasible ideas to engineering fields, especially robotics. Immune system plays important roles to maintain its own system against dynamically changing environments. Therefore, we believe that immune system would provide a new paradigm suitable for dynamic problem dealing with unknown environments rather than static problem. In this paper, we propose a new approach to behavior-based AI by paying attention to biological immune system. We confirm the feasibility of our proposed method by applying to a behavior control of an autonomous mobile robot in cluttered environment as a practical example.

Conventional AI (artificial intelligence) technology has been criticized on many drawbacks such as brittleness under dynamically changing environments. To overcome this problem, other approaches called behavior-based AI, new AI, emergent computation, animat approach and so on were proposed and confirmed their usefulness. Since computational ability of mobile robots is limited in general, it is required that robots extract feasible information from their environment for appropriate behavior control by themselves. On the other hand, in living organisms, there are mainly two systems to cope with dynamically changing environment: 1) immune system, and 2) emotional system. Based on this fact, in this paper, we present a new method of behavior arbitration mechanisms for autonomous mobile robots by paying close attention to emotional system in living organisms. We confirm the feasibility of our proposed method by applying to an obstacle avoidance problem of a mobile robot as a practical example.

Strong demands for developing autonomous decentralized systems have arisen as systems have been increasing in their scale and complexity. On the other hand biological systems such as human beings can be said to be the ultimate decentralized system, and are expected to provide feasible ideas in engineering fields. Immune systems work as on-line fault diagnosis systems by constructing self-nonself recognition networks. In this paper the authors try to apply this immunological self-nonself recognition networks to gait acquisition of a 6-legged walking robot as a practical example. The feasibility of the proposed method is confirmed by simulations.>