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Biological inspiration has long been pursued as a key to more efficient, agile and elegant control in robotics. It has been a successful strategy in the design and control of robots with both biologically abstracted and biomimetic designs. Behavioral studies have resulted in a good understanding of the mechanics of certain animals. However, without a better understanding of their nervous systems, the biologically-inspired observation-based approach was limited. The findings of Hess and Buschges, and Ekeberg et al. describing the neural mechanisms of stick insect intra-leg joint coordination have made it possible to control models of insect legs with a network of neural pathways they found in the animal's thoracic ganglia. Our work with this model, further informed by cockroach neurobiological studies performed in the Ritzmann lab, has led to LegConNet (Leg Controller Network). In this paper we show that LegConNet controls the forward stepping motion of a robotic leg. With hypothesized additional pathways, some later confirmed by neurobiology, it can smoothly transition the leg from forward stepping to turning movements. We hypothesize that commands descending from a higher center in the nervous system inhibit or excite appropriate local neural pathways and change thresholds, which, in turn, create a cascade of reflexes resulting in behavioral transitions.

Motivated by biological inspiration and the issue of code disruption, we develop a new form of LGP called Parallel LGP (PLGP). PLGP programs consist of n lists of instructions. These lists are executed in parallel, after which the resulting vectors are combined to produce program output. PGLP limits the disruptive effects of crossover and mutation, which allows PLGP to significantly outperform regular LGP.

Genetic programming (GP) has been successfully applied to solving multiclass classification problems, but the performance of GP classifiers still lags behind that of alternative techniques. This paper investigates an alternative form of GP, Linear GP (LGP), which demonstrates great promise as a classifier as the division of classes is inherent in this technique. By combining biological inspiration with detailed knowledge of program structure two new crossover operators that significantly improve performance are developed. The first is a new crossover operator that mimics biological crossover between alleles, which helps reduce the disruptive effect on building blocks of information. The second is an extension of the first where a heuristic is used to predict offspring fitness guiding search to promising solutions.

When deriving bio-inspired algorithms, the ''probes'' of Openness, Diversity, Interaction, Structure and Scale (ODISS) can form the basis of a systematic analysis. In our work, we apply ODISS to both the biological inspiration (the mammalian immune system) and the application area, Adaptive Information Filtering (AIF). This approach allows a principled analysis and development of a complex bio-inspired approach to AIF.

The aim of this work is to develop a new compliant mechanism [1–3]. The observation of the snail (helix pomatia L.) provides the biological inspiration of a compliant mechanism consisting of silicone. For this an analytic model was generated. With the finite element method (FEM) the influence of design parameter variation and the variation of the used compliant material on motion behavior of the structure center under increasing internal pressure load was investigated. The motion behavior of the first prototype showed an acceptable quantitative and qualitative correlation with the simulation results.

This paper serves as a short introduction for the special PerMIS session on Biological Inspiration (BI) for Intelligent Systems. The paper is organized into 4 parts. Part 1 provides a brief introduction to the idea and history of bio-inspiration for computation. Part 2 discusses the increased relevance of bio-understanding and presents a few examples of increased bio-understanding in cognitive areas. Part 3 provides examples of alternate focuses and levels of bio-reality that BI can take. Finally Part 4 notes some potential challenges in BI for intelligent systems.

O presente trabalho apresenta um Algoritmo Transgenético para a solução do Problema do Caixeiro Comprador Capacitado Simétrico. O algoritmo é baseado em endossimbiose e outras transformações do fluxo intracelular. O algoritmo é descrito e experimentos computacionais são relatados no sentido de validar a eficiência da abordagem. São também apresentadas novas melhores soluções para 17 instâncias de um conhecido banco de instâncias para o problema.This paper presents a Transgenetic Algorithm to solve the Symmetric Capacitated Traveling Purchaser Problem. The biological inspiration of the proposed algorithm comes from the endosymbiotic theory of evolution. The algorithm is described and the results of computational experiments are reported. The performance of the algorithm is compared with other recent work presented in the literature. New best solutions for 17 instances of a known benchmark are reported.

In this paper we present the architecture for the Personal Autonomic Desktop Manager, a self managing application designed to act on behalf of the user in several aspects: protection, healing, optimization and configuration. The overall goal of this research is to improve the correlation of the autonomic self* properties and doing so also enhance the overall self-management capacity of the desktop (autonomicity). We introduce the Circulatory Computing (CC) model, a self-managing system initiative based on the biological metaphor of the cardiovascular system, and use its concepts in the design and implementation of the architecture.

Recently, P2P (Peer-to-Peer) streaming systems have attracted great attention. In P2P streaming systems, streaming data such as video and audio are divided into a number of small pieces for efficient data distribution. This approach allows peers to collect the pieces from each other while playing the streaming data. Our investigation into ecological systems suggests that the piece collection in P2P streaming systems is similar to the bee's nectar collection and that their nectar collecting behavior can be used as a model in designing piece collection methods for P2P streaming systems. In this paper, we propose data collection methods for P2P streaming systems inspired by bee's nectar collecting behavior. There are several types of bees in the bee ecology, which exhibit different behavior, and accordingly, we propose three different methods inspired by three typical bee types; those are honey, bumble, and carpenter bees. Suitable environments for the three methods may differ and are investigated in this paper.

Many algorithms are the result of biological inspiration and particle swarm optimization (PSO) is no exception. However, the PSO algorithm has slightly different end goals to the biological behavior that provides its inspiration and so needs to differ from its biological inspiration in some, perhaps nonbiological, ways. PSO takes its inspiration from the flocking of birds and fish. In the real world, the flock needs to be compact for protection, and once food is found the flock should settle to feed. In the artificial particle swarm optimization the aim of the algorithm is to find an optimum solution to some problem, rather than the protection or food sought in the natural environment. For PSO the correct behavior once an optimum is found is not for all the particles in the swarm to converge on this, possibly local, optimum as the goal is to check many optima in the hope of finding the global optimum. Instead of converging, once an optimum has been found, it should be noted and the particles should immediately disperse to look for another, perhaps better, optimum. In Nature the time will come when a swarm that is feeding has consumed the food so that the place is no longer optimal: if swarming for protection the threat may change or even disappear completely. Then the swarm will again set out. Such extended periods of convergence serve no useful purpose so far as an artificial swarm is concerned.

Biological systems at all scales self-assemble and maintain themselves in dynamic states away from thermodynamic equilibrium. Examination of various biological examples of dynamic self-assembly (DySA) enables one to extract general principles that govern it. These principles, in turn, can be applied to the rational design of artificial DySA systems by combining biological inspiration with the tools of chemistry, physics, and engineering. Such artificial DySA ensembles can be studied in quantitative detail and will ultimately lead to novel applications in biomimetic “smart” structures and materials.

Artificial cells have generated much interest since the concept was introduced by Aleksandr Oparin in the 1920s, and they have had an impact on the pharmaceutical and biotechnology industry in various areas, including potential therapeutic applications. Here, we discuss the development of small-scale, bio-inspired artificial cell components that recreate the function of key cellular and physiological systems. We describe artificial cells, selected current applications and how small-scale biology could be used to provide what might be a next-generation approach in this area. We believe that this type of work is in its infancy and that exploiting small-scale biological inspiration in the field of artificial cells has great potential for successes in the future.

We propose a theoretical framework for the design of multiagent systems with the capacity to surprise a human observer. Reasons for the difficulty in creating simulations capable of perpetual innovation are addressed. Artificial life concepts and biological inspiration from different abstraction layers serve as base for a three part model. These parts are: world modelling with artificial chemistries, agent brains and population dynamics. The parts of the model are theoretically discussed and some academic examples provided. Possible applications to biological research in the problem of speciation are considered.

Artificial immune systems (AISs) to date have generally been inspired by naive biological metaphors. This has limited the effectiveness of these systems. In this position paper two ways in which AISs could be made more biologically realistic are discussed. We propose that AISs should draw their inspiration from organisms which possess only innate immune systems, and that AISs should employ systemic models of the immune system to structure their overall design. An outline of plant and invertebrate immune systems is presented, and a number of contemporary research that more biologically-realistic AISs could have is also discussed., Comment: 12 pages, 6th International Conference on Artificial Immune Systems (ICARIS2007), Lecture Notes in Computer Science 4628, Santos, Brazil

Humans have always looked to nature for design inspiration, and material design on the molecular level is no different. Here we explore how this idea applies to nanoscale biomimicry, specifically examining both recent advances and our own work on engineering lipid and polymer membrane systems with cellular processes.

Greater understanding of biology in modern times has enabled significant breakthroughs in improving healthcare, quality of life, and eliminating many diseases and congenital illnesses. Simultaneously there is a move towards emulating nature and copying many of the wonders uncovered in biology, resulting in "biologically inspired" systems. Significant results have been reported in a wide range of areas, with systems inspired by nature enabling exploration, communication, and advances that were never dreamed possible just a few years ago. We warn, that as in many other fields of endeavor, we should be inspired by nature and biology, not engage in mimicry. We describe some results of biological inspiration that augur promise in terms of improving the safety and security of systems, and in developing self-managing systems, that we hope will ultimately lead to self-governing systems

Autonomic computing and other self-managing system initiatives, many strongly based on biological metaphors, are emerging as a significant new vision for the design and development of complex computer systems. They offer the promise of controlling complexity through the achievement of self governance (autonomy) and self management (autonomicity). We consider how complexity is exhibited in the computer industry as a whole, and how the situation is deteriorating, rather than improving. We consider how autonomous and autonomic systems, with their biological inspiration, can provide a framework for tackling complexity and overcoming the problems of its (unavoidable) inherent existence in certain classes of systems.

Artificial haircell (AHC) sensor is presented for highly sensitive flow-field measurements. Design considerations and MEMS process flows are given. Oscillating flow field measurements show sensitivity down to 0.6 mm/s flow rates, steady state flow fields detected down to 0.1 mm/s.

To realise fish-like swim patterns by a robotic system poses tremendous challenges. This requires fully understanding of fish biomechanics and the way to mimic it. This paper presents our research toward the sensor-based control of autonomous robotic fish that can swim in a 3D unstructured environment, based on observations of fish swim behaviours. Our robotic fish has a tail with three or four degrees of freedom (DOF) and is controlled by 4 onboard computers (a powerful Gumstix Linux PC and 3 PICs) and over 10 embedded sensors. Both simulated and the real fish experiments are conducted to show the feasibility and performance of the proposed approach.

This study is based on the observation and imitation of nature. It is expected, by studying live walking animals, to find interesting parameters that could be used in various fields of robotics, especially the four-legged locomotion. Another goal is to find parameters that don't vary from one type of animal to another. This could be used to build a virtual walking model of a disappeared animal just from its known geometry for example. The steps described here are the different experiments on live animals, the conception of a computer model, and some results obtained with dynamic simulations. Different animals have been studied in the past like the hedgehog, the tenrec and the shrew, and a first model has been achieved. A new model, which is based on the tortoise of Hermann, consists in a 15 links, 22 degrees of freedom system, animated with proportional-derivative control, which references are actual recordings of live animals

This thesis deals with the design of multi-agent systems. It focuses on formalism based approach and aims in the long run to build, automatically and in a decentralized way, the behaviours of cooperative agents which must solve a collective problem. The goal of this work was to propose new techniques to build the behaviour of social agents, able to consider the presence of other agents in the system.Existing formalism like DEC-POMDPs manage to formalize multi-agents problem but they don't represent at the agent level the concept of interaction which is fundamental in collective systems. It induces a important complexity in the algorithms used to build the behaviours of the agents. In order to give the agent the ability to consider the presence of other agents in the system and to structure implicitely multi-agents systems, this thesis proposes an original formalism the Interac-DEC-POMDP inspired by the DEC-POMDP formalism and Hamelin, a simulation developped during this thesis and inspired by collective biological phenomenon. The specificity of this new formalism lies in the ability given to the agents to interact directly and locally among them. It allows them to make decision at a level between global level and individual level.Furthermore, we have proposed a decentralized algorithm based on reinforcement learning techniques and on distribution of individual rewards among agents during interactions. We have conducted experiments and validated our proposal : this algorithm manage to produce adaptive collective behaviour without the need for the agents to have a global vision of the system.; Cette thèse traite de la conception de système multi-agents. Elle se focalise sur des approches formelles et s'est donné pour objectif à long terme de construire de manière automatique et décentralisée les comportements d'agents coopératifs devant résoudre collectivement un problème. Ce travail a cherché à proposer des méthodes pour construire les comportements d'agents sociaux, capables de prendre en compte à l'exécution la présence d'autres agents dans le système.Les formalismes existants comme les DEC-POMDPs parviennent à représenter des problèmes multi-agents mais ne représentent pas au niveau individuel la notion d'interaction fondamentale dans les systèmes collectifs. Ceci induit une complexité algorithmique importante dans les algorithmes de résolution. Afin de donner aux agents la possibilité d'appréhender la présence d'autres agents et de structurer de manière implicite les systèmes multi-agents, cette thèse propose un formalisme original, l'interac-DEC-POMDP inspiré des DEC-POMDPs et d'Hamelin, une simulation développée au cours de cette thèse et issue d'expériences conduites en éthologie. La spécificité de ce formalisme réside dans la capacité offerte aux agents d'interagir directement et localement entre eux. Cette possibilité permet des prises de décision à un niveau intermédiaire entre des décisions globales impliquant l'ensemble des agents et des décisions purement individuelles.Nous avons proposé en outre un algorithme décentralisé basé sur des techniques d'apprentissage par renforcement et une répartition heuristique des gains des agents au cours des interactions. Une démarche expérimentale nous a permis de valider sa capacité à produire pour des restriction du formalisme des comportements collectifs pertinents adaptatifs sans qu'aucun agent ne dispose d'une vue globale du système.

The reactive multi-agent approach emphasizes individual simplicity over the collective complexity of the task being performed. However, to apply such an approach to a problem, the components of the multi-agent system have to be designed in such a way that the society be able to fulfill its requirements with a reasonable efficiency. Inspiration from natural self-organized systems is a way to solve this conception issue. This article illustrates two cases of how natural self-organized systems can be transposed to engineer societies of agents that collectively solve problems. It presents two original self organized models conceived in cooperation with biologists and details how transposition principles have been used to design collective problem solving systems.

Biomimetic robotics is a rapidly developing field, and the limited literature about biological inspiration in robot grasping at cognitive level suggests that the field has still much to offer. Neuroscience studies indicate that vision-based reaching and grasping are important to the extent that an entire cortical pathway is dedicated to these skills. Nevertheless, recent findings point out the existence of strict relations between action-oriented (dorsal pathway) and categorization-oriented (ventral pathway) vision. In this paper, we will compare present day research on vision-based robotic grasping with the above mentioned neuroscience findings. Then, we propose a new approach to vision for grasping in robotics, which aims at improving the emulation of human skills through the integration of the information flows proceeding from the two visual pathways.

Colloque avec actes et comité de lecture. internationale.; International audience; This paper introduces a multi-layer software architecture, that allows smart design and implementation of complex cortical neural networks, and efficient parallel execution on multiprocessor machines. The developer implements cortical networks sticking to their natural fine grained formalism, without caring about its mapping on coarse grained parallel computers. The developer can use a set of graphical tools to easily develop and debug the cortical systems. Some experiments of new cortical model design on multiprocessor PCs are introduced, and some performance measurements are given. This software suite is operational and currently in use in our laboratory to control a robotic am through cortical neural networks running on multiprocessor PCs.

Biological inspiration in the design of computing machines could allow the creation of new machines with promising characteristics such as fault-tolerance, self-replication or cloning, reproduction, evolution, adaptation and learning, and growth. The aim of this paper is to introduce bio-inspired computing tissues that might constitute a key concept for the implementation of 'living' machines. We first present a general overview of bio-inspired systems and the POE model that classifies bio-inspired machines along three axes. The Embryonics project--inspired by some of the basic processes of molecular biology--is described by means of the BioWatch application, a fault-tolerant and self-repairable watch. The main characteristics of the Embryonics project are the multicellular organization, the cellular differentiation, and the self-repair capabilities. The BioWall is intended as a reconfigurable computing tissue, capable of interacting with its environment by means of a large number of touch-sensitive elements coupled with a color display. For illustrative purposes, a large-scale implementation of the BioWatch on the BioWall's computational tissue is presented. We conclude the paper with a description of bio-inspired computing tissues and POEtic machines.

Visual systems in the animal kingdom are incredibly good at extracting useful information from what can often be a very complicated world. Many of these systems can provide inspiration for the design of our own ’seeing machines’ which we can then use in a variety of applications. Our own research is concerned with the detection of ’looming’ or motion in depth. Our biological inspiration is the locust, Locusta migratoria, which possesses two uniquely identifiable neurons (the LGMD and DCMD) that respond preferentially to movements directly towards the animal. The way in which these cells are able to identify such stimuli is now becoming well understood. As such, we have been able to create a plausible computational model of the afferent inputs to these neurons that has been shown to respond in a locust-like way to looming stimuli. This model is now being used to control the movements of a mobile robot within a simplified visual environment. We aim to continue the development of this model so that it may one day function within the same visual world as the locust itself.

Rapport interne.; Ce document présente une partie du travail de thèse effectué par Vincent Thomas au sein de l'équipe MAIA. Il s'attarde sur deux points - une simulation nommée Hamelin d'un phenomene de specialisation observe dans des groupes de rats soumis à des contraintes environnementales - une réutilisation des principes de cette organisation dans des systèmes markoviens pour la prise en compte des interactions directes entre agents.

Colloque avec actes et comité de lecture. nationale.; National audience; L'apprentissage par renforcement devient difficile dans le cadre multi-agent car il faut prendre en compte la multiplicité des points de vue. Cet article présente une simulation inspiré d'un phénomène de spécialisation observé dans des groupes de rats. La simulation, nommée Hamelin, modèlise un certains nombre de mécanismes permettant à une collectivité de s'organiser. Nous présentons dans cet article ces mécanismes et mettons en évidence les liens qui peuvent exister entre cette simulation et le formalisme des MDP et l'apprentissage par renforcement avec l'idée que l'inspiration biologique peut aider à découvrir de nouveaux mécanismes pour un apprentissage par renforcement collectif.

Tightrope walkers use poles to keep their balance. A study reveals that agama lizards use their tails much like balancing poles as they leap through the air — and that some dinosaurs may have done the same. See Letter p.181 In 1969 — a few years after unearthing the first Velociraptor fossil — John Ostrom speculated that theropod dinosaurs used their tails as dynamic stabilizers during active or irregular movements. A study combining computer modelling, video observation of leaping agama lizards (Agama agama) and the construction of a robot with a lizard-like tail provides support for Ostrom's hypothesis. The results show that, using sensory feedback, active tails can stabilize body attitude mid-air by transferring angular momentum from body to tail. The inertia of swinging appendages has also been invoked as a stabilizing factor in primates and other animals, so these findings are relevant to our understanding of appendage evolution in general. They may also provide biological inspiration for the design of highly manoeuvrable search-and-rescue robots using tails.

Colloque avec actes et comité de lecture. internationale.; International audience; Our team is working on the modeling of biologically inspired artificial neural networks. These models are derived from existing models in theoretical biology or built from collaborations with neuroscientists. They are used to endow robots with different cognitive capacities related to autonomous behavior, like perception, sensorimotor coordination, exploration, planning, reasoning, and other tasks linked to memory and internal representation building. As engineers, we have to adapt these models to have them effective on real robots and real tasks. The most difficult aspect in this task is to integrate different models designed for different cognitive aspects like for example feature extraction and planning. To do so, we have to wonder about the levels of time involved in behavioral schemes and to integrate the corresponding models accordingly. Now, as a feedback to biology, we propose to discuss these levels of time and to relate them first to biological mechanisms and second to fundamental properties like information coding, learning, planning, motivation.

Colloque avec actes et comité de lecture. internationale.; International audience; Reactive problem solving is a way to propose systems composed of simple interacting agents that collectively solve problems outside the scope of individual perceptions. In this domain, natural social systems are sources of inspiration for simple mechanisms. This article presents an approach to region detection inspired by social spiders. Based on a behavioral model determined by the simulation of collective weaving, we describe how we transposed it to obtain an approach for region detection in gray level images and we propose a first assessment of the approach.

Colloque avec actes et comité de lecture. internationale.; International audience; Les phénomènes collectifs en biologie sont une source d'inspiration pour proposer des méthodes multi-agents de résolution de problèmes. Ils permettent l'élaboration de comportements individuels simples produisant collectivement des phénomènes complexes. Cet article expose la démarche de transposition que nous avons suivie pour passer d'un modèle de simulation à un modèle de résolution. Le cadre biologique initial concerne un processus de construction collective de toile chez les araignées sociales. Sa transposition concerne la détection de régions dans des images à niveau de gris

Colloque avec actes et comité de lecture. internationale.; International audience; Biological inspiration has led to the design of many connectionist models and mechanisms. Among them, memorization mechanisms are of particular importance to endow biologically inspired systems with efficient and consistent adaptive abilities. In this paper, we report recent modelling works of this kind, implementing procedural, episodic and working memories and making them cooperate for autonomous agent navigation.

Nature’s technologies should not be copied blindly, but used to inspire design. Nature’s lessons on legged locomotion involve energy exchange, transfer, storage and return. Animals operating as low degree of freedom, spring-loaded, inverted pendulums can capitalize on the passive, dynamic self-stabilization possible from many legs and a sprawled posture. Recent advancements in system controls and materials manufacturing promise to permit the application of nature’s lessons to engineered robots.

Our lives are essentially dominated by genes. They govern our physical features, our behaviour, our personalities, our health, and indeed our longevity. The recent greater understanding of genetics has proved to be a vital tool for genetic engineering applications in many disciplines, in addition to medicine and agriculture. It is well known that genes can be manipulated, controlled and even turned on and off in order to achieve desirable amino acid sequences of a polypeptide chain.

The complexity and fragility of next-generation hardware will require novel design approaches and tools.

We present the results of a recent collaboration between scientists, engineers and chefs. Two particular devices are developed, both inspired by natural phenomena reliant on surface tension. The cocktail boat is a drink accessory, a self-propelled edible boat powered by alcohol-induced surface tension gradients, whose propulsion mechanism is analogous to that employed by a class of water-walking insects. The floral pipette is a novel means of serving small volumes of fluid in an elegant fashion, an example of capillary origami modeled after a class of floating flowers. The biological inspiration and mechanics of these two devices are detailed, along with the process that led to their development and deployment.

Textural and rheological characteristics of foods are known to profoundly affect the swallowing process. Food technologists continue to exploit this notion in the management of symptomatic swallowing disorders (dysphagia) where novel foods are designed to elicit more reliable transport characteristics. Currently, little is understood about the relationship between food bolus formulation and its flow-induced interactions with the swallowing tract. Experimentation of a medical nature in this field is extremely challenging, and may put patients at risk. In the rheological domain the deformation fields are dissimilar to that of the biological system. In response to these limitations, quantitative assessment of bolus transport by a novel rheometric testing device is proposed. This paper describes the inspiration for a biologically-inspired robotic swallowing device to be applied to address these issues. This will allow for an improved understanding of swallowing mechanics and food design in the engineering, medical, and food technology fields.

Each paper in this special feature extends the work presented at the TAROS conference (Towards Autonomous Robotic Systems) in September 2007 at Aberystwyth University in the UK. The first paper, by Lenz et al, implements a biologically inspired adaptive gaze controller. The second, by Edwards et al, takes inspiration from the human skin corpuscular sensors to build an artificial finger for texture discrimination. TAROS, now in its 10th year, continues as a premier platform for the UK robotics research community to meet, present ideas and discuss all issues concerning robotics. The remit of TAROS is wide, from biologically inspired robotics to kinematics, and participants from all areas are welcome. We were also pleased to invite two internationally renowned speakers to Aberyswtyth for the 2007 conference (Hugh Durrant-Whyte from the University of Sydney, and Jean-Arcady Meyer from the Animat Lab in Paris) and also welcomed many international participants who brought a wider perspective to the proceedings. Until last year the EPSRC funded network, Biro-Net, supported TAROS to encourage growth and integration of research in the area of biologically inspired robotics. It was with great pleasure that the input from the biologically inspired robotics community allowed a complete session on the subject areas within the TAROS conference in 2007. The first paper, which won the Institute of Physics prize for the best biologically inspired paper presented at the TAROS conference, is by A Lenz, T Balakrishnan, A G Pipe and Chris Melhuish. This paper presents a hardware implementation of the vestibulo-ocular stabilizing reflex present in many vertebrates. In a biological system, this reflex allows stable vision even during dynamic changes such as disease, growth or fatigue. The authors aim to implement these useful capabilities based on a biologically plausible model on a robotic testbed. The paper begins with a discussion on gaze stabilization and describes in detail the biological basis for the model used on the robotic platform. The paper continues with an introduction to the model used, placing it in context with the previous section. The mechanical testbed is introduced, along with an on-line learning algorithm designed to reduce the perceived visual slip from its gyroscope. The paper presents some experimental results and discusses the performance of the robotic implementation of the vestibulo-ocular reflex. The second paper, by J Edwards, J Lawry, J Rossiter and C Melhuish, follows suit with a hardware implementation of another human sensor, the touch sense corpuscles under the skin. The aim of the paper is to differentiate between the textures from a variety of different surface types, a useful skill for a robotic sensor. The work is placed in context with reference to other similar sensors, and the biological inspiration is examined. A series of disks were produced with varying surfaces which were run in turn under an artificial finger (all produced using rapid prototyping) to produce a series of vibration recordings. These were then classified using feature analysis which was then compared with principal component analysis. We would like to thank IOP Publishing and especially Andrew Malloy for their continued support of the TAROS conference.

In this article, we present the BioWall, a giant reconfigurable computing tissue developed to implement machines according to the principles of our Embryonics (embryonic electronics) project. The BioWall's size aid features are designed for public exhibition, but at the same time it represents an invaluable research tool, particularly since its complete programmability and cellular structure are extremely well adapted to the implementation of many different kinds of bio-inspired systems. To illustrate these capabilities, we present a set Of applications that range over many diverse sources of biological inspiration, from Embryonics' ontogenetic systems, through epigenetic artificial neural networks, to phylogenetic evolving hardware. All these applications have been fully implemented and tested in hardware on the BioWall.

Our approach to biologically inspired materials and materials systems recognizes biology (at all scale levels) as a series ofproductsthat fulfill particular functions. It then links material composition and structure tofunctionthroughpropertiesand therefore attempts to bring mechanism to processes andfunctionsof biology. As an example of this approach we have focused on the lipid bilayer membranes of blood cells, like erythrocytes and neutrophils, as a bioinspired material system for drug delivery leading to the creation of waxy, nano capsules called liposomes that can be triggered to release their drug by hyperthermia. Thus, while Nature's encapsulation technology provides the inspiration, the mechanism of drug release is non-natural. The necessary design parameters for the required functions of drug encapsulation, i.e. drug retention, circulation half life, and eventual thermally-triggered drug release, were obtained through extensive experimentation and modeling of artificial lipid vesicles by us and others, with much of the mechanical and thermomechanical properties, molecular exchange, andin vitroperformance investigated by a direct micropipet manipulation technique. With respect to cancer chemotherapy, the unmet need for primary solid tumors is to deliver more drug to the tumor tissue thereby reducing the tumor size (debulking) while at the same time reducing toxic side effects. It is with these criteria in mind that we developed the temperature-triggered liposome for the treatment of solid tumors. This paper then, describes this liposome development and its performancein vivo, where, in some cases, the temperature-triggered release of drug directly in the blood stream and tumor resulted in complete tumor regression. What this example also shows is that through material property measurement and modeling, new insights into Nature's functions and designs can be discovered in areverse engineeringprocess from which new products can then beforward engineeredto solve engineering and product problems in health, technology, and the environment.

In nature, many small animals use jumping locomotion to move in rough terrain. Compared to other modes of ground locomotion, jumping allows an animal to overcome obstacles that are relatively large compared to its size. In this thesis we outline the main design challenges that need to be addressed when building miniature jumping robots. We then present three novel robotic jumpers that solve those challenges and outperform existing similar jumping robots by one order of magnitude with regard to jumping height per size and weight. The robots presented in this thesis, called EPFL jumper v1, EPFL jumper v2 and EPFL jumper v3 have a weight between 7g and 14.3g and are able to jump up to 27 times their own size, with onboard energy and control. This high jumping performance is achieved by using the same mechanical design principles as found in jumping insects such as locusts or fleas. Further, we present a theoretical model which allows an evaluation whether the addition of wings could potentially allow a jumping robot to prolong its jumps. The results from the model and the experiments with a winged jumping robot indicate that for miniature robots, adding wings is not worthwhile when moving on ground. However, when jumping from an elevated starting position, adding wings can lead to longer distances traveled compared to jumping without wings. Moreover, it can reduce the kinetic energy on impact which needs to be absorbed by the robot structure. Based on this conclusion, we developed the EPFL jumpglider, the first miniature jumping and gliding robot that has been presented so far. It has a mass of 16.5g and is able to jump from elevated positions, perform steered gliding flight, land safely and locomote on ground with repetitive jumps1. ______________________________ 1See the collection of the accompanying videos at http://lis.epfl.ch/microglider/moviesAll.zip