Introduction to the Special Issue on Morphological Computation
Helmut Häuser, Hidenobu Sumioka, Rudolf Marcel Füchslin, Rolf Pfeifer · Artificial Life · 2012
The purposeful action of any agent in a complex environment requires control, that is, the determination of specific sequences of values for the parameters determining the state of the agent. In recent years, it became clear that we have to extend our notion of control if we want to understand the mechanical and chemical process management of biological systems. As it turns out, the lessons we learn from this extension can be directly used in engineering—foremost in the field of robotics, but increasingly also in other areas, such as artificial life or novel types of chemical systems design. The extension we refer to includes the intrinsic dynamics of the system to be controlled as an active, even computational element of control. The employment of the physical or chemical dynamics of a system as part of the computations necessary for control is the underlying principle of the concept of morphological computation. In our use of the term “morphology” we include all aspects of a physical system, not only the shape of the agent, the geometrical combination of the body parts, but also its material properties, such as friction coefficients or parameters describing compliance. Moreover, we also consider the distribution of sensors and actuators as part of the morphology.Conventionally (and with slight oversimplification), control is understood in terms of an agent (typically the term plant is used) and a separate entity, which is the control unit and consists of a conventional digital computer. The state space of the agent is spanned by a number of parameters, which in the case of robots are mechanical in nature, but in the case of chemical cells can be densities, phase parameters, and so on. The controller possesses, via its sensors, an (at least partial) internal representation of the state of the agent at any time. In addition, the controller can change the state of the agent via some actuators. In an ideal situation, the controller exerts complete control over the agent, which means it can determine the agent's position in state space with high accuracy. In such an ideal situation, the observable behavior is the result of a conventional computation based on the internal representation of the agent in the controller. However, there is always some noise present in the dynamics of the agent. Moreover, if the model of the agent gets too complex (high-dimensional state space, high nonlinearity, noise, etc.), the required controller may be intractable. To avoid both problems, that is, to keep the agent controllable, an often followed strategy (during the design process) consists in the reduction of the number of degrees of freedom of the agent. For example, in the case of robots this is reflected by the use of rigid body parts, which are connected by high-torque servos.Control based on morphological computation pursues a different strategy. There is still a division into agent and controller. However, the controller is no longer assumed to exert complete control, and therefore the line of separation gets fuzzy. In extreme cases (e.g., mechanically driven walking machines like passive walkers [1]) this separation can even vanish completely. Morphological computation requires that the physical (chemical) dynamics of the agent be designed in such a way that the complexity of the task that is solved by the controller is minimized. This happens by outsourcing parts of the computation to the physical body (more on the precise meaning of outsourcing later). In a simple example the body dynamics of the agent provide various limit cycles, and the task of the controller is reduced to choosing the right basin of attraction and initiating jumps into it. The choice will clearly depend on the demands made by the environment and/or the prescribed task. For example, in the case of a locomoting robot the dynamics of the body implicitly take over tasks such as the stabilization of the movement pattern against small perturbations, while the controller simply has to choose an appropriate gait. Stated differently, the number of tasks to be fulfilled by the conventional controller is reduced. This reduction can be understood as an outsourcing of computation to the body, since the control problem as such (e.g., how to handle small perturbations caused by an uneven floor in the case of walking machines) is still the same. Notably, not only can a clever exploitation of the physical dynamics of an agent reduce the amount of computation to be performed by the controller, but also the information necessary for the internal representation of the agent's state is reduced. As soon as the stabilization of a movement pattern is intrinsic to the body dynamics of the moving agent, the controller only needs to know in which basin of attraction the agent actually resides. No details about positions, velocities, and so on are necessary. In consequence, the computations that remain to the controller are not necessarily full simulations of the dynamics of the agent, but relatively simple evaluations such as “If condition A is satisfied and agent resides in basin of attraction B, jump into basin C”—a task that is mastered by a simple finite state automaton.It should be emphasized that morphological computation is more than clever systems design. Our claim that computation can be outsourced to the body and not just omitted is corroborated by a recent result of Hauser et al. [3, 4]. They demonstrated that a sufficiently complex physical body equipped with a simple readout (e.g., a feed-forward neural network or even only a linear and static readout) is computationally equivalent to a conventional device almost as powerful as a Turing machine. This finding has two major implications. The first one is that computation can be outsourced to the body in a quite literal sense. The second one is relevant for the engineer's choice of the design of an agent. There is a whole spectrum of options for distributing computations between the physical dynamics of the agent and a conventional control device. It is quite plausible that the optimal choice will not be found at either end of this spectrum. As briefly mentioned in the introduction, the agents under consideration need not be classical, macroscopic robots. It is well known that chemical process management in eukariotic cells results from an interplay between molecular, supramolecular, and mesoscopic structures (e.g., membranes or the cytoskeleton) and their dynamic interaction. For an illustrative example, consider protein sorting by vesicular transport [5].Recently, simple forms of cognition and motility have been implemented in artificial systems in the laboratory; see [2]. Such systems, besides being of fundamental interest for questions concerning the origin of life as well as modern bionanoengineering, also shed light on a form of morphological control not directly visible in the macroscopic world. In the study of their dynamics, classical mechanics is replaced, or at least complemented, by statistical physics. Furthermore, collective phenomena, such as phase transitions, start to play an important role.Morphological computation can be found on a wide range of different scales in a number of different contexts. Consequently, it has attracted many scientists from various backgrounds. For example, roboticists are interested in adopting this concept to build more energy-efficient and robust robots. Biologists try to understand the functions of body parts from the viewpoint of morphology (i.e., morphofunctionality). Computational scientists aim to capture the implications a physical implementation of certain types of computation has for computational theory as a whole. Scientists in the field of medicine have been attracted by the idea of morphological computation. For example, the decrease of movement control in elderly patients can, from the viewpoint of morphological computation, be interpreted as the loss of (morpho)computational power and is not necessarily the consequence of decreasing neural activity. The cause of this loss can be found in a change of the mechanical properties of the body—a change that also alters the attractor landscape in the state space of the body. As a result of aging, this attractor landscape may become less pronounced (e.g., self-stabilization of movement patterns may become slower) and be more difficult to navigate.The interdisciplinarity of the research community suggests that morphological computation is a very general concept. On one hand, this represents a significant challenge when we try to capture the concept in a unified theory or even if we want to define the boundaries of the field itself. On the other hand, it provides us with numerous research opportunities, especially in the interdisciplinary, overlap areas of research. We believe morphological computation even has the potential to give rise to completely new fields of research. To harvest this inherent potential of our community, we organized the 2nd International Conference on Morphological Computation (http://morphcomp.org/) in September, 2011, in Venice. As one of the results of the conference we initiated this special issue. The conference committee and all invited speakers, all experts in the field, were asked to vote for their favorite submissions among the accepted abstracts. Based on this poll, we invited the corresponding authors to submit an extended journal version of their work.As a result, we have in total nine accepted articles, which provide us with a snapshot of recent research done in the context of morphological computation. Furthermore, they also point to new directions of research in the field. While some of the articles have a focus on engineering (e.g., building a series of real robots to investigate the influence of morphology on locomotion, as in Reis et al.'s article), others are oriented toward more general considerations and point to potential future research directions. One from the latter category is the submission by Herrera and Sanz. Their article illustrates impressively how morphological computation attracts an interdisciplinary audience. The background of the authors is found in the theory of emotion. They suggest linking emotions with changes in the morphology and the resulting changed functionality. For example, if the emotion of fear arises, the body changes, namely, it gets ready—either to fight or to flee. As a consequence, the body can no longer be described as a fixed dynamical system, but has to been seen as a variable dynamical structure, which changes its state of attraction (from a finite and discrete set) depending on the emotion it embodies at the moment. This approach implies a new way of looking at the embodiment in robotics and constitutes a new challenge for the design and control of such physical bodies. Although such emotion-induced changes will take place on a slower timescale than the usual movements, the control is nontrivial. The controller will have to produce discrete switches between analogue systems in a stable fashion. The authors suggest some potential control theoretical approaches; however, we might have to rethink the control approach for such systems entirely.Another article, which also belongs to the more general category of this special issue, is the one by Füchslin et al. It consists of two parts: First, it presents and discusses a formalization of the concept of morphological computation. The authors build upon the Venice definition1 given at the First International Conference on Morphological Computation. They clarify the notions of result and input, and the notion and role of a program in morphological computation. Such a formalization is helpful when one aims at a better understanding of what it means to program a morphological computation. It is often illustrated using attractor landscapes of dynamical systems. Füchslin et al. discuss also what it means if the controller (the brain) can alter the attractor landscape. They interpret, for example, training in various sport activities not as learning how to move, but learning how to bring the body into a morphology (internal tension, geometry) that is optimally suited for a given task. Based on different types of feedback between agent and controller (body and brain), they present a hierarchy of levels of morphological computation. In a second part, they present three partially ongoing case studies of systems employing morphological computation and control in different contexts. A first case study deals with exoskeletal support systems for people with movement impairments. In a second investigation, they analyze self-assembly processes from the viewpoint of morphological computation. The third study is more speculative and discusses aspects of cellular dynamics in a specific type of combination therapy in oncology with respect to control.Umedachi et al. investigated morphological computation on the level of single-cell organisms—specifically, the true slime mold. This is a large amoeba-like unicellular organism that does not have any nervous it a of In to understand how such complex patterns can any even any simple neural they implemented a simple physical In their simulations they changes of behavior as and by mechanically the agent, the parameters of the model itself. 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