Search

Interfacing robots with real biological systems is a potential approach to realizing truly adaptive machines, which is a long-standing engineering challenge. Although plants are widely spread and versatile, little attention has been given to creating cybernetic systems incorporating plants. Producing such systems requires two main steps: the acquisition and interpretation of biological signals, and issuing the appropriate stimulation signals for controlling the physiological response of the biological part. We investigate an automated physiological recovery of young avocado plants by realizing a closed interaction loop between the avocado plant and a water-control device. The study considers the two aforementioned steps by reading out postural cues (leaf inclination) and electrophysiological (biopotential) signals from the plant, and controlling the water resource adaptive to the drought condition of an avocado plant. Analysis of the two signals reveals time-frequency patterns of increased power and global synchronization in the narrow bands when water is available, and local synchronization in the broad bands for water shortage. The results indicate the feasibility of interface technologies between plants and machines, and provide preliminary support for achieving adaptive plant-based ‘machines’ based on plants’ large and robust physiological spectrum and machines’ control scheme diversity. We further discuss fundamental impediments hindering the use of living organisms like plants for artificial systems. [ABSTRACT FROM PUBLISHER]

The behaviors of the animals or embodied agents are characterized by the dynamic coupling between the brain, the body, and the environment. This implies that control, which is conventionally thought to be handled by the brain or a controller, can partially be outsourced to the physical body and the interaction with the environment. This idea has been demonstrated in a number of recently constructed robots, in particular from the field of "soft robotics". Soft robots are made of a soft material introducing high-dimensionality, non-linearity, and elasticity, which often makes the robots difficult to control. Biological systems such as the octopus are mastering their complex bodies in highly sophisticated manners by capitalizing on their body dynamics. We will demonstrate that the structure of the octopus arm cannot only be exploited for generating behavior but also, in a sense, as a computational resource. By using a soft robotic arm inspired by the octopus we show in a number of experiments how control is partially incorporated into the physical arm's dynamics and how the arm's dynamics can be exploited to approximate non-linear dynamical systems and embed non-linear limit cycles. Future application scenarios as well as the implications of the results for the octopus biology are also discussed. [ABSTRACT FROM AUTHOR]

Animals and humans engage in an enormous variety of behaviors which are orchestrated through a complex interaction of physical and informational processes: The physical interaction of the bodies with the environment is intimately coupled with informational processes in the animal's brain. A crucial step toward the mastery of all these behaviors seems to be to understand the flows of information in the sensorimotor networks. In this study, we have performed a quantitative analysis in an artificial agent -- a running quadruped robot with multiple sensory modalities -- using tools from information theory (transfer entropy). Starting from very little prior knowledge, through systematic variation of control signals and environment, we show how the agent can discover the structure of its sensorimotor space, identify proprioceptive and exteroceptive sensory modalities, and acquire a primitive body schema. In summary, we show how the analysis of directed information flows in an agent's sensorimotor networks can be used to bootstrap its perception and development. [ABSTRACT FROM AUTHOR]

Grasp stability in the human hand has been resolved by means of an intricate network of mechanoreceptors integrating numerous cues about mechanical events, through an ontogenetic grasp practice. An engineered prosthetic interface introduces considerable perturbation risks in grasping, calling for feedback modalities that address the underlying slip phenomenon. In this study, we propose an enhanced slip feedback modality, with potential for myoelectric-based prosthetic applications, that relays information regarding slip events, particularly slip occurrence and slip speed. The proposed feedback modality, implemented using electrotactile stimulation, was evaluated in psychophysical studies of slip control in a simplified setup. The obtained results were compared with vision and a binary slip feedback that transmits on–off information about slip detection. The slip control efficiency of the slip speed display is comparable to that obtained with vision feedback, and it clearly outperforms the efficiency of the on–off slip modality in such tasks. These results suggest that the proposed tactile feedback is a promising sensory method for the restoration of stable grasp in prosthetic applications. [ABSTRACT FROM PUBLISHER]

Self-assembly is a phenomenon broadly observed in nature where a vast number of various molecules spontaneously synthesize complex structures. In this article, prompted by the need for the realization of highly autonomous self-assembly systems that employ magnetism as a driving force, we discuss fundamental issues associated with magnetically driven self-assembly systems. We first introduce some examples from our case studies, in which the models all subscribe to a distributed approach, and thus lack central control. Then we categorize them by their type of magnetic attachment. The issues discussed include several fundamental properties, such as the effect of morphology, stochasticity, the difference between two-dimensional models and three-dimensional models, emergence, allostericity, and parallelism. The conclusions obtained support our stance that the appropriate morphology lightens the control cost for the assembly, providing primal but engaging instances of magnetic self-assembly systems that warrant further study. [ABSTRACT FROM PUBLISHER]

The decay in structure size of manufacturing products has yielded new demands on spontaneous composition methods. The key for the realization of small-sized robots lies in how to achieve the efficient assembly sequence in a bottom-up manner, where most of the parts have only limited (or no) computational (i.e. deliberative) abilities. In this paper, based on a novel self-assembly platform consisting of self-propulsive centimetre-sized modules capable of aggregation on the surface of water, we study the effect of stochasticity and morphology (shape) on the yield of targeted formations in self-assembly processes. Specifically, we focus on a unique phenomenon: that a number of modules instantly compose a target product without forming intermediate subassemblies, some of which constitute undesired geometrical formations (termed one-shot aggregation). Together with a focus on the role that the morphology of the modules plays, we validate the effect of one-shot aggregation with a kinetic rate mathematical model. Moreover, we examined the degree of parallelism of the assembly process, which is an essential factor in self-assembly, but is not systematically taken into account by existing frameworks. [ABSTRACT FROM AUTHOR]

Self-assembly is a key phenomenon whereby vast numbers of individual components passively interact and form organized structures, as can be seen, for example, in the morphogenesis of a virus. Generally speaking, the process can be viewed as a spatial placement of attractive and repulsive components. In this paper, we report on an investigation of how morphology, i.e., the shape of components, affects a self-assembly process. The experiments were conducted with 3 differently shaped floating tiles equipped with magnets in an agitated water tank. We propose a novel measure involving clustering coefficients, which qualifies the degree of parallelism of the assembly process. The results showed that the assembly processes were affected by the aggregation sequence in their early stages, where shape induces different behaviors and thus results in variations in aggregation speeds. [ABSTRACT FROM AUTHOR]

Presents information relating to adaptive behavior, providing background details on the neurobiology of insect polarization vision. Description of a hardware implementation of polarization-sensitive neurons; Neural basis of polarization vision in insects; Processing of a polarized light in the insect nervous system.

The author comments on the study by researcher Barbara Webb on the effectiveness of building models of existing animals rather than building ad hoc artificial creature. He agrees on the vitalness of an animat validation and proposes alternative validation criteria to guide comparison with an animal. He opposes the idea regarding the immunity of animats to criticisms of their validity as the only apparent advantage of their use and stresses that animats are of use for biologically relevant study.

Soft machines have recently gained prominence due to their inherent softness and the resulting safety and resilience in applications. However, these machines also have disadvantages, as they respond with complex body dynamics when stimulated. These dynamics exhibit a variety of properties, including nonlinearity, memory, and potentially infinitely many degrees of freedom, which are often difficult to control. Here, we demonstrate that these seemingly undesirable properties can in fact be assets that can be exploited for real-time computation. Using body dynamics generated from a soft silicone arm, we show that they can be employed to emulate desired nonlinear dynamical systems. First, by using benchmark tasks, we demonstrate that the nonlinearity and memory within the body dynamics can increase the computational performance. Second, we characterize our system's computational capability by comparing its task performance with a standard machine learning technique and identify its range of validity and limitation. Our results suggest that soft bodies are not only impressive in their deformability and flexibility but can also be potentially used as computational resources on top and for free. [ABSTRACT FROM AUTHOR]

Complex systems involving many interacting elements often organize into patterns. Two types of pattern formation can be distinguished, static and dynamic. Static pattern formation means that the resulting structure constitutes a thermodynamic equilibrium whose pattern formation can be understood in terms of the minimization of free energy, while dynamic pattern formation indicates that the system is permanently dissipating energy and not in equilibrium. In this paper, we report experimental results showing that the morphology of elements plays a significant role in dynamic pattern formation. We prepared three different shapes of elements (circles, squares, and triangles) floating in a water-filled container, in which each of the shapes has two types: active elements that were capable of self-agitation with vibration motors, and passive elements that were mere floating tiles. The system was purely decentralized: that is, elements interacted locally, and subsequently elicited global patterns in a process called self-organized segregation. We showed that, according to the morphology of the selected elements, a different type of segregation occurs. Also, we quantitatively characterized both the local interaction regime and the resulting global behavior for each type of segregation by means of information theoretic quantities, and showed the difference for each case in detail, while offering speculation on the mechanism causing this phenomenon. [ABSTRACT FROM AUTHOR]