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Spiders dedicate a significant amount of energy and a variety of different types of silk to build their webs. Therefore, it has been speculated that spider webs are more than just simple traps. Spider web are highly complex mechanical structures that might act also as mechanical filters that significantly enhance and extend the sensory capabilities of spiders,enabling them to identify and localise preys and predators. This paper experimentally investigates the dynamic behaviour of a synthetic, 3D-printed spider web to identity general morphological features that can contribute to this filtering capability. First results show, even a simple, artificial web exhibits a wide range of complex dynamic behaviours such as sub- and super-harmonic interactions

In this paper we present NeatSkin, a novel artificial skin sensor based on electrical impedance tomography. The key feature is a discrete network of fluidic channels which is used to infer the location of touch. Change in resistance of the conductive fluid within these channels during deformation is used to construct sensitivity maps. We present a method to simulate touch using this unique network-based, low output dimensionality approach. The efficacy is demonstrated by fabricating a NeatSkin sensor. This paves the way for the development of more complex channel networks and a higher resolution soft skin sensor with potential applications in soft robotics, wearable devices and safe human-robot interaction.

Inspired by natural growing processes, we investigate how morphological changes can potentially help to lead and facilitate the task of learning to control a robot. We use the model of a tadpole that grows in four discrete stages into a frog. The control task to learn is to locomote to food positions that occur at random positions. We employ reinforcement learning, which is able to find a tail-driven swimming strategy for the tadpole stage that transitions into a leg-driven strategy for the frog. Furthermore, by using knowledge transferred from one growing stage to the next one, we were able to show that growing can benefit from guiding the controller optimization through morphological changes. The results suggest that learning time can be reduced compared to the cases when learning each stage individually from scratch.

Recently, it has been reported that the dynamics of mechanical structures can be used as a computational resource—also referred to as morphological computation. In particular soft materials have been shown to have the potential to be used for time series forecasting. Although most soft materials can be modeled by mass-spring systems, a limited number of researches has been performed on the computational capabilities of such systems. In this paper, we propose an array of masses linked in a gridlike structure by spring-damper connections to investigate systematically the influence of structural (size) and dynamic (stiffness, damping) parameters on the computational capabilities for time series forecasting. In addition, such a structure gives us a good approximation of two-dimensional elastic media, e.g., a rubber sheet, and therefore a direct pathway to potentially implement results in a real system. In particular, we compared the mass-spring array to echo state networks, which are standard machine learning techniques for this kind of problems and are also closely related to the underlying theoretical models applied when exploiting mechanical structures for computation. Our results suggest a clear connection of morphological features to computational capabilities.

Morphological Computation is a concept used in robotics that sees physical bodies of robots as means to carry out computations that are relevant for their successful interaction with the environment. It is inspired by observations in nature where we can see that the morphology (i.e. the shape as well the dynamic properties of the body) of biological systems is playing a crucial role for the emergence of intelligent behavior. Although there are a number of successful implementations of this concept in robotics, there are still challenges to overcome. One is that any functionality implemented in a morphology is deemed to be fixed. However, truly autonomous robots should be highly flexible and are expected to be able to adapt to changes in the environment and to new tasks. In case of morphological computation, in order to change the desired computation to be carried out, the underlying morphology has to be altered. A solution is to introduce mechanisms that enable the robot to make these changes online, often referred to as morphosis. We introduce and discuss a general notion of morphosis from the view point of dynamical systems theory, highlight the concept by examples from robotics, and elaborate on the wide-reaching implications with respect to the design of highly autonomous robots.

The robot dance has, since its inception in 1967, been a caricature of how robots move. Its imitation of the series of precise, linear motions with abrupt starts and stops is instantly recognizable. Despite a certain wavering in its popularity, it remains part of modern culture. What is so remarkable about the robot dance is that it mimics the movements and constraints of conventional rigid robots, a type of robot that is ubiquitous in automated manufacturing and object handling, and it could so easily have been invented now. It also strikingly shows that industrial robotics have relied on conventional hard technologies for more than 40 years and still does. However, this is about to change. The last few years has seen a rapid growth in soft robotics, technologies that are fundamentally different from their rigid counterparts. Soft robotics is set to have a major impact on all aspects of our society and industries, ranging from manufacturing and consumer devices to medical applications and wearable technology.

Recently, two theoretical models for morphological computation have been proposed [13,14]. Based on a rigorous mathematical framework and simula- tions it has been demonstrated that compliant, complex physical bodies can be ef- fectively employed as computational resources. Even more recent work showed that these models are not only of theoretical nature, but are also applicable to a number of different soft robotic platforms. Motivated by these encouraging results we dis- cuss a number of remarkable implications when real physical bodies are employed as computational resources.

Robot arms equipped with an additional network of masses and springs enable a facilitation of control by the exploitation of morphological properties. Typically, these mass-spring networks are being generated randomly. This approach comes in its basic form unhandy when the network should obey specific constraints, such as spring lengths, angles between springs or, e.g., the physical expansion range of the network. We present an approach of emulating a growth process on the basis of L-systems. Therefore, we have developed a simulation environment to define L-systems and instructions to translate the produced strings into mass-spring networks.

Keynote lecture Today, the concept of morphological computation, i.e. the idea that the control of a system is at least partially embodied in its functional physical or chemical dynamics, has become a well - established approach in robotics. Novel designs that exhibit robust behavior and adaptability result from a blurring of the distinction between hard- and software. Such designs are often inspired by biology: Not only by the means (i.e. materials) of nature but also by the ways (organizational principles) nature employs. Remarkably, morphological control connects technology and biology in a bi – directional manner. We apply the concepts of morphological control in order to support, complement and substitute physiological processes in medical problem settings. Thereby, we exploit the ideas of morphological control in systems on different length scales and governed by classical as well as statistical mechanics. In a first group of investigations, we aim at the implementation of an inflatable support system for patients with movement impairments. Quite often, the loss of the ability to control one’s movements is interpreted as a decrease of the computational power of neural system. Based on experimental evidence, we hypothesize that at least in some cases neural control is still at its full power. But due to changes of the mechanical properties of the aging body, the control problem has become more difficult, i.e. the body experiences a decrease of its morphological control power. By the use of inflatable exoskeletal devices, we aspire to influence the mechanical properties of the body in such a manner that morphological control is re – established. The inflatable structures we employ are based on the “tensairity” principle. Recent research demonstrated the feasibility of actuated soft structures based on tenairity. In that sense, soft robotics meets morphological control in the medical field. On the microscopic level, we focus on cell – dynamics. We assume the cell as a system that is controlled by the genetic apparatus as well as its mesoscopic structure. The chemical networks (as well as supramolecular and membrane structures) constitute a complex dynamical system with an attractor landscape. Genetic signals may push the cell from one basin of attraction into another one or influence the attractor landscape. Based on this approach, we study the effect of synergistic therapies in oncology (hyperthermia – radio therapy) and formulate models that allow an optimization of these therapies. The models we use are based on a small number of phenomenological parameters which can be mapped on those parameters determining the details of the therapy applied. The main problem we are presently addressing is whether our basic assumption, namely that the generically high – dimensional dynamics of the cell can be represented by a low – dimensional model, is really justified and how we have to choose the parameters of the model such that an optimization can be achieved.