Results 1 - 10 of 433

Animating Human Athletics

by Jessica K. Hodgins , Wayne L. Wooten, David C. Brogan, James F. O'Brien , 1995
"... This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorith ..."
Abstract - Cited by 348 (23 self) - Add to MetaCart
This paper describes algorithms for the animation of men and women performing three dynamic athletic behaviors: running, bicycling, and vaulting. We animate these behaviors using control algorithms that cause a physically realistic model to perform the desired maneuver. For example, control algorithms allow the simulated humans to maintain balance while moving their arms, to run or bicycle at a variety of speeds, and to perform a handspring vault. Algorithms for group behaviors allow a number of simulated bicyclists to ride as a group while avoiding simple patterns of obstacles. We add secondarymotion to the animations with springmass simulations of clothing driven by the rigid-body motion of the simulated human. For each simulation, we compare the computed motion to that of humans performing similar maneuvers both qualitatively through the comparison of real and simulated video images and quantitatively through the comparison of simulated and biomechanical data.
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Competitive Coevolution through Evolutionary Complexification

by Kenneth O. Stanley, Risto Miikkulainen - Journal of Artificial Intelligence Research , 2002
"... Two major goals in machine learning are the discovery of complex multidimensional solutions and continual improvement of existing solutions. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demons ..."
Abstract - Cited by 204 (72 self) - Add to MetaCart
Two major goals in machine learning are the discovery of complex multidimensional solutions and continual improvement of existing solutions. In this paper, we argue that complexification, i.e. the incremental elaboration of solutions through adding new structure, achieves both these goals. We demonstrate the power of complexification through the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves increasingly complex neural network architectures. NEAT is applied to an open-ended coevolutionary robot duel domain where robot controllers compete head to head. Because the robot duel domain supports a wide range of sophisticated strategies, and because coevolution benefits from an escalating arms race, it serves as a suitable testbed for observing the effect of evolving increasingly complex controllers. The result is an arms race of increasingly sophisticated strategies. When compared to the evolution of networks with fixed structure, complexifying networks discover significantly more sophisticated strategies. The results suggest that in order to realize the full potential of evolution, and search in general, solutions must be allowed to complexify as well as optimize.
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A Taxonomy for Artificial Embryogeny

by Kenneth O. Stanley, Risto Miikkulainen , 2003
"... A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evoluti ..."
Abstract - Cited by 195 (51 self) - Add to MetaCart
A major challenge for evolutionary computation is to evolve phenotypes such as neural networks, sensory systems, or motor controllers at the same level of complexity as found in biological organisms. In order to meet this challenge, many researchers are proposing indirect encodings, that is, evolutionary mechanisms where the same genes are used multiple times in the process of building a phenotype. Such gene reuse allows compact representations of very complex phenotypes. Development is a natural choice for implementing indirect encodings, if only because nature itself uses this very process. Motivated by the development of embryos in nature, we define Artificial Embryogeny (AE) as the subdiscipline of evolutionary computation (EC) in which phenotypes undergo a developmental phase. An increasing number of AE systems are currently being developed, and a need has arisen for a principled approach to comparing and contrasting, and ultimately building, such systems. Thus, in this paper, we develop a principled taxonomy for AE. This taxonomy provides a unified context for long-term research in AE, so that implementation decisions can be compared and contrasted along known dimensions in the design space of embryogenic systems. It also allows predicting how the settings of various AE parameters affect the capacity to efficiently evolve complex phenotypes.

Automatic Definition of Modular Neural Networks

by Frederic Gruau , 1995
"... This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnet ..."
Abstract - Cited by 179 (4 self) - Add to MetaCart
This paper illustrates an artificial developmental system that is a computationally efficient technique for the automatic generation of complex Artificial Neural Networks (ANN). Artificial developmental system can develop a graph grammar into a modular ANN made of a combination of more simple subnetworks. A genetic algorithm is used to evolve coded grammars that generates ANNs for controlling a six-legged robot locomotion. A mechanism for the automatic definition of sub-neural networks is incorporated. Using this mechanism, the genetic algorithm can automatically decompose a problem into subproblems, generate a subANN for solving the subproblem, and instantiate copies of this subANN to build a higher level ANN that solves the problem. We report some simulation results showing that the same problem cannot be solved if the mechanism for automatic definition of sub-networks is suppressed. We support our argumentation with pictures describing the steps of development, how ANN structures ar...

Tracking The Red Queen: Measurements of adaptive progress in co-evolutionary simulations

by Dave Cliff, Geoffrey F. Miller - In , 1995
"... . Co-evolution can give rise to the "Red Queen effect", where interacting populations alter each other's fitness landscapes. The Red Queen effect significantly complicates any measurement of co-evolutionary progress, introducing fitness ambiguities where improvements in performance of ..."
Abstract - Cited by 163 (3 self) - Add to MetaCart
. Co-evolution can give rise to the "Red Queen effect", where interacting populations alter each other's fitness landscapes. The Red Queen effect significantly complicates any measurement of co-evolutionary progress, introducing fitness ambiguities where improvements in performance of co-evolved individuals can appear as a decline or stasis in the usual measures of evolutionary progress. Unfortunately, no appropriate measures of fitness given the Red Queen effect have been developed in artificial life, theoretical biology, population dynamics, or evolutionary genetics. We propose a set of appropriate performance measures based on both genetic and behavioral data, and illustrate their use in a simulation of co-evolution between genetically specified continuous-time noisy recurrent neural networks which generate pursuit and evasion behaviors in autonomous agents. 1 Introduction Some biologists have suggested that the `Red Queen effect' arising from coevolutionary arms races has been a p...
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Evolving morphologies of simulated 3d organisms based on differential gene expression

by Peter Eggenberger - PROCEEDINGS OF THE FOURTH EUROPEAN CONFERENCE ON ARTI CIAL LIFE, ECAL97 , 1997
"... Most simulations of biological evolution depend on a rather restricted set of properties. In this paper a richer model, based on differential gene expression is introduced to control developmental processes in an artificial evolutionary system. Differential gene expression is used to get different c ..."
Abstract - Cited by 154 (1 self) - Add to MetaCart
Most simulations of biological evolution depend on a rather restricted set of properties. In this paper a richer model, based on differential gene expression is introduced to control developmental processes in an artificial evolutionary system. Differential gene expression is used to get different cell types and to modulate cell division and cell death. One of the advantages using developmental processes in evolutionary systems is the reduction of the information needed in the genome to encode e.g. shapes or cell types which results in better scaling behavior of the system. My result showed that the shaping of multicellular organisms in 3d is possible with the proposed system.

Challenges in Evolving Controllers for Physical Robots

by Maja Mataric, Dave Cliff , 1996
"... This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising direction ..."
Abstract - Cited by 153 (6 self) - Add to MetaCart
This paper discusses the feasibility of applying evolutionary methods to automatically generating controllers for physical mobile robots. We overview the state of the art in the field, describe some of the main approaches, discuss the key challenges, unanswered problems, and some promising directions. 1 Introduction This paper is concerned with the distant goal of automated synthesis of robot controllers. Specifically, we focus on the problems of evolving controllers for physically embodied and embedded systems that deal with all of the noise and uncertainly present in the world. We will also address some systems that evolve both the morphology and the controller of a robot. Within the scope of this paper we define morphology as the physical, embodied characteristics of the robot, such as its mechanics and sensor organization. Given that definition, the only examples of evolving both morphology and control exist in simulation. Evolutionary methods for automated hardware design are an ...

Central pattern generators for locomotion control in animals and robots: a review

by Auke Jan Ijspeert - NEURAL NETWORKS , 2008
"... The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic ..."
Abstract - Cited by 140 (20 self) - Add to MetaCart
The problem of controlling locomotion is an area in which neuroscience and robotics can fruitfully interact. In this article, I will review research carried out on locomotor central pattern generators (CPGs), i.e. neural circuits capable of producing coordinated patterns of high-dimensional rhythmic output signals while receiving only simple, lowdimensional, input signals. The review will first cover neurobiological observations concerning locomotor CPGs and their numerical modelling, with a special focus on vertebrates. It will then cover how CPG models implemented as neural networks or systems of coupled oscillators can be used in robotics for controlling the locomotion of articulated robots. The review also presents how robots can be used as scientific tools to obtain a better understanding of the functioning of biological CPGs. Finally, various methods for designing CPGs to control specific modes of locomotion will be briefly reviewed. In this process, I will discuss different types of CPG models, the pros and cons of using CPGs with robots, and the pros and cons of using robots as scientific tools. Open research topics both in biology and in robotics will also be discussed. 1
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Evolutionary robotics: the Sussex approach

by I. Harvey, P. Husbands, D. Cliff, A. Thompson, N. Jakobi - ROBOTICS AND AUTONOMOUS SYSTEMS , 1997
"... ... the last 5 years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots; simulated robots, coevolved animats, real robots ..."
Abstract - Cited by 131 (14 self) - Add to MetaCart
... the last 5 years. We explain and justify our distinctive approaches to (artificial) evolution, and to the nature of robot control systems that are evolved. Results are presented from research with evolved controllers for autonomous mobile robots; simulated robots, coevolved animats, real robots with software controllers, and a real robot with a controller directly evolved in hardware.

Creating High-Level Components with a Generative Representation for Body-Brain Evolution

by Gregory S. Hornby, Jordan B. Pollack , 2002
"... One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translatio ..."
Abstract - Cited by 125 (20 self) - Add to MetaCart
One of the main limitations of scalability in body-brain evolution systems is the representation chosen for encoding creatures. This paper defines a class of representations called generative representations, which are identified by their ability to reuse elements of the genotype in the translation to the phenotype. This paper presents an example of a generative representation for the concurrent evolution of the morphology and neural controller of simulated robots, and also introduces GENRE, an evolutionary system for evolving designs using this representation. Applying GENRE to the task of evolving robots for locomotion and comparing it against a non-generative (direct) representation shows that the generative representation system rapidly produces robots with significantly greater fitness. Analyzing these results shows