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16th Ibero-American Conference on Artificial Intelligence (IBERAMIA)

作者： Martins, Gustavo Urbano, Paulo Christensen, Anders Lyhne Univ Lisbon BioISI Fac Ciencias Lisbon Portugal IUL ISCTE Lisbon Portugal Inst Telecomuncacoes Lisbon Portugal

ISBN: (纸本)9783030039288;9783030039271

In evolutionary robotics role allocation studies, it is common that the role assumed by each robot is strongly associated with specific local conditions, which may compromise scalability and robustness because of the dependency on those conditions. To increase scalability, communication has been proposed as a means for robots to exchange signals that represent roles. This idea was successfully applied to evolve communication-based role allocation for a two-role task. However, it was necessary to reward signal differentiation in the fitness function, which is a serious limitation as it does not generalize to tasks where the number of roles is unknown a priori. In this paper, we show that rewarding signal differentiation is not necessary to evolve communication-based role allocation strategies for the given task, and we improve reported scalability, while requiring less a priori knowledge. Our approach puts fewer constrains on the evolutionary process and enhances the potential of evolving communication-based role allocation for more complex tasks.

关键词： Role allocation Collective robotics Swarm NEAT Fitness function Artificial evolution evolutionary robotics

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Hierarchical Behavioral Repertoires with Unsupervised Descriptors 18

Hierarchical Behavioral Repertoires with Unsupervised Descri...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Cully, Antoine Demiris, Yiannis Imperial Coll London London England

ISBN: (纸本)9781450356183

Enabling artificial agents to automatically learn complex, versatile and high-performing behaviors is a long-lasting challenge. This paper presents a step in this direction with hierarchical behavioral repertoires that stack several behavioral repertoires to generate sophisticated behaviors. Each repertoire of this architecture uses the lower repertoires to create complex behaviors as sequences of simpler ones, while only the lowest repertoire directly controls the agent's movements. This paper also introduces a novel approach to automatically define behavioral descriptors thanks to an unsupervised neural network that organizes the produced high-level behaviors. The experiments show that the proposed architecture enables a robot to learn how to draw digits in an unsupervised manner after having learned to draw lines and arcs. Compared to traditional behavioral repertoires, the proposed architecture reduces the dimensionality of the optimization problems by orders of magnitude and provides behaviors with a twice better fitness. More importantly, it enables the transfer of knowledge between robots: a hierarchical repertoire evolved for a robotic arm to draw digits can be transferred to a humanoid robot by simply changing the lowest layer of the hierarchy. This enables the humanoid to draw digits although it has never been trained for this task.

关键词： Behavioral repertoires Quality-diversity optimization evolutionary robotics

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Investigating an A-star Algorithm-based Fitness Function for Mobile Robot Evolution 22

Investigating an A-star Algorithm-based Fitness Function for...

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22nd International Conference on System Theory, Control and Computing (ICSTCC)

作者： Prabhu, Shanker G. R. Kyberd, Peter Wetherall, Jodie Univ Greenwich Dept Engn Sci Chatham ME4 4TB Kent England

ISBN: (纸本)9781538644447

One of the factors that affect the success of evolutionary robotics (ER) is the way fitness functions are designed to operate. While needs-based custom fitness functions have been developed, most of the time they have been defined in simpler mathematical functions to reduce the computation time. In this paper, we hypothesize that an incremental fitness function based on established techniques in specific task domains in robotics will aid the evolution process. An A-star algorithm-based fitness function for path planning is designed and implemented for evolving the body plans and controllers of robots for navigation and obstacle avoidance tasks. It has been shown that using this concept, fitter robots have evolved in most cases when compared to simple distance-only based fitness functions. However, due to variable performance of the evolver with the A-star fitness function, the results are inconclusive. We also identify problems associated with the fitness function and make recommendations for designing future fitness functions based on observations of the experiments.

关键词： evolutionary robotics Co-evolution Fitness Function A-star Algorithm

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Towards the Targeted Environment-Specific Evolution of Robot Components 18

Towards the Targeted Environment-Specific Evolution of Robot...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Collins, Jack Geles, Wade Howard, David Maire, Frederic CSIRO Brisbane Qld Australia Queensland Univ Technol Brisbane Qld Australia

ISBN: (纸本)9781450356183

This research considers the task of evolving the physical structure of a robot to enhance its performance in various environments, which is a significant problem in the field of evolutionary robotics. Inspired by the fields of evolutionary art and sculpture, we evolve only targeted parts of a robot, which simplifies the optimisation problem compared to traditional approaches that must simultaneously evolve both (actuated) body and brain. Exploration fidelity is emphasised in areas of the robot most likely to benefit from shape optimisation, whilst exploiting existing robot structure and control. Our approach uses a Genetic Algorithm to optimise collections of Bezier splines that together define the shape of a legged robot's tibia, and leg performance is evaluated in parallel in a high-fidelity simulator. The leg is represented in the simulator as 3D-printable file, and as such can be readily instantiated in reality. Provisional experiments in three distinct environments show the evolution of environment-specific leg structures that are both high-performing and notably different to those evolved in the other environments. This proof-of-concept represents an important step towards the environment-dependent optimisation of performance-critical components for a range of ubiquitous, standard, and already-capable robots that can carry out a wide variety of tasks.

关键词： evolutionary robotics Simulation Parallelization Genetic Algorithm Bezier Spline Environmental Adaptation

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Comparing Approaches for Evolving High-level Robot Control based on Behaviour Repertoires

Comparing Approaches for Evolving High-level Robot Control b...

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IEEE Congress on evolutionary Computation (IEEE CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Gomes, Jorge Christensen, Anders Lyhne Univ Lisbon Fac Ciencias BioISI BioMachines Lab Lisbon Portugal Inst Univ Lisboa ISCTE IUL BioMachines Lab Inst Telecomunicacoes Lisbon Portugal

ISBN: (纸本)9781509060177

evolutionary robotics approaches have traditionally been focused on monolithic controllers. Recent studies on the evolution of hierarchical control have, however, yielded promising results. Hierarchical approaches typically rely on a repertoire of behaviour primitives (which themselves can be the result of an evolutionary process), and an evolved top-level arbitrator that continually executes primitives from the repertoire to solve a given task. In this paper, we compare different controller architectures for the evolution of top-level arbitrators. We propose two new methods, one based on neural networks and another based on decision trees induced by genetic programming. We compare the new approaches with existing ones, namely neural network regressors and non-hierarchical control, in a challenging simulated maze navigation task that requires a broad diversity of primitives. Based on empirical results, we draw a number of conclusions regarding the strengths and limitations of each of the studied approaches.

关键词： evolutionary robotics neural networks genetic programming behaviour repertoires hierarchical control

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Evolving a Repertoire of Controllers for a Multi-function Swarm 21st

Evolving a Repertoire of Controllers for a Multi-function Sw...

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21st International Conference on the Applications of evolutionary Computation (EvoApplications)

作者： Engebraten, Sondre A. Moen, Jonas Yakimenko, Oleg Glette, Kyrre Norwegian Def Res Estab POB 25 N-2027 Kjeller Norway Univ Oslo POB 1080 N-0316 Oslo Blindern Norway Naval Postgrad Sch 1 Univ Circle Monterey CA 93943 USA

ISBN: (纸本)9783319775388

Automated design of swarm behaviors with a top-down approach is a challenging research question that has not yet been fully addressed in the robotic swarm literature. This paper seeks to explore the possibility of using an evolutionary algorithm to evolve, rather than hand code, a wide repertoire of behavior primitives enabling more effective control of a large group or swarm of unmanned systems. We use the MAP-elites algorithm to generate a repertoire of controllers with varying abilities and behaviors allowing the swarm to adapt to user-defined preferences by selection of a new appropriate controller. To test the proposed method we examine two example applications: perimeter surveillance and network creation. Perimeter surveillance require agents to explore, while network creation requires them to disperse without losing connectivity. These are distinct application that have drastically different requirements on agent behavior, and are a good benchmark for our swarm controller optimization framework. We show a performance comparison between a simple weighted controller and a parametric controller. Evolving controllers allows for specifying desired behaviors top-down, in terms of objectives to solve, rather than bottom-up.

关键词： Swarm UAVs MAP-elites Multi-function evolutionary robotics

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NEUROEVOLUTION IN CONTROL OF INTELLIGENT SYSTEMS: BENCHMARK TESTING, SIMULATED AND PHYSICAL DEMONSTRATIONS

NEUROEVOLUTION IN CONTROL OF INTELLIGENT SYSTEMS: BENCHMARK ...

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作者： Sharat Parameswaran Chidambaran University at Buffalo State University of New York

学位级别：硕士

A majority of Artificial Neural Network (ANN) implementations in autonomous systems use a fixed/user-prescribed network topology, leading to sub-optimal performance and low portability. The existing neuro-evolution of augment- ing topology or NEAT paradigm offers a powerful alternative by allowing the network topology and the connection weights to be simultaneously optimized through an evolutionary process. In this thesis, we will discuss the use of a modified neuroevolution paradigm, inspired largely by NEAT. Most NEAT im- plementations allow the consideration of only a single objective. There also persists the question of how to tractably introduce topological diversification that mitigates overfitting to training scenarios. The modified neuroevolution algorithm seeks to address these shortcomings. While adopting the basic ele- ments of NEAT, important modifications are made to the selection, speciation, and mutation processes. With the backdrop of small-robot path-planning ap- plications, an experience-gain criterion is derived to encapsulate the amount of diverse local environment encountered by the system. This criterion facilitates the evolution of genes that support exploration, thereby seeking to generalize from a smaller set of mission scenarios than possible with performance max- imization alone. The effectiveness of the single-objective (optimizing perfor- mance) and the multi-objective (optimizing performance and experience-gain) neuro-evolution approaches are evaluated on two different small-robot cases, with ANNs obtained by the multi-objective optimization observed to provide superior performance in unseen scenarios. Finally, we will also discuss and analyze the usage of this modified neuroevolution algorithm on two different, difficult benchmark problems as well as discuss its potential use in the design of a high level intelligence model of a one-of-a-kind energy autonomous robot.

关键词： Artificial Neural Networks Neuroevolution evolutionary robotics Multiobjective Optimization Neuroevolution through Augmenting Topologies

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evolutionary Mission Planning

Evolutionary Mission Planning

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IEEE Congress on evolutionary Computation

作者： Rahul Kala Abeer Khan D. Diksha S. Shelly Surabhi Sinha Robotics and Artificial Intelligence Laboratory Indian Institute of Information Technology Allahabad Allahabad India

The problem of mission planning enables a robot to solve for complex missions and thereafter execute the missions. The problem is seen as an advancement of the classical problem of motion planning wherein the task is to find a trajectory for a robot to go from a configuration A to a configuration B avoiding obstacles. The popular mechanism to solve the problem is using Temporal Logic specifications to specify a mission, and to thereafter use search strategies to solve the mission. The same requires an exponential complexity in terms of propositional variables to search and verify the mission plan. Further, optimality is usually not a criterion in finding a solution, and the transition costs are usually ignored leading to sub-optimal mission plans. As missions get more complex using a large number of propositional variables for specification, it may no longer be possible to use exponential complexity algorithms. The paper proposes the use of evolutionary Computation to solve the same problem. We first develop a new restricted language for mission design. Even though the language is restrictive, it can specify a large number of missions of real life service robotics. Then we design an evolutionary computation framework to solve the mission. Probabilistic Roadmap technique is used to get the transition system and transition costs between regions of interest. The mission planner takes the mission specification and these transition costs to compute a mission plan. The mission plan is executed using a reactive navigator that can avoid any dynamic obstacle, other people and robots.

关键词： evolutionary robotics Genetic Algorithm Robot Motion Planning Service robotics

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Neurorobotics-A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots

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FRONTIERS IN NEUROrobotics 2018年 12卷 42页

作者： Krichmar, Jeffrey L. Univ Calif Irvine Dept Cognit Sci Irvine CA 92717 USA Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

Neurorobots are robots whose control has been modeled after some aspect of the brain. Since the brain is so closely coupled to the body and situated in the environment, Neurorobots can be a powerful tool for studying neural function in a holistic fashion. It may also be a means to develop autonomous systems that have some level of biological intelligence. The present article provides my perspective on this field, points out some of the landmark events, and discusses its future potential.

关键词： brain-based devices evolutionary robotics embodied cognition cognitive robotics Neural Darwinism neuromorphic engineering

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Scalable co-optimization of morphology and control in embodied machines

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JOURNAL OF THE ROYAL SOCIETY INTERFACE 2018年第143期15卷 20170937-20170937页

作者： Cheney, Nick Bongard, Josh SunSpiral, Vytas Lipson, Hod Cornell Univ Dept Computat Biol & Biol Stat Ithaca NY 14850 USA Univ Wyoming Dept Comp Sci Laramie WY 82071 USA Univ Vermont Dept Comp Sci Burlington VT 05405 USA NASA Ames SGT Inc Intelligent Syst Div Intelligent Robot Grp Mountain View CA USA Columbia Univ Dept Mech Engn New York NY 10027 USA

Evolution sculpts both the body plans and nervous systems of agents together over time. By contrast, in artificial intelligence and robotics, a robot's body plan is usually designed by hand, and control policies are then optimized for that fixed design. The task of simultaneously co-optimizing the morphology and controller of an embodied robot has remained a challenge. In psychology, the theory of embodied cognition posits that behaviour arises from a close coupling between body plan and sensorimotor control, which suggests why co-optimizing these two subsystems is so difficult: most evolutionary changes to morphology tend to adversely impact sensorimotor control, leading to an overall decrease in behavioural performance. Here, we further examine this hypothesis and demonstrate a technique for 'morphological innovation protection', which temporarily reduces selection pressure on recently morphologically changed individuals, thus enabling evolution some time to 'readapt' to the new morphology with subsequent control policy mutations. We show the potential for this method to avoid local optima and converge to similar highly fit morphologies across widely varying initial conditions, while sustaining fitness improvements further into optimization. While this technique is admittedly only the first of many steps that must be taken to achieve scalable optimization of embodied machines, we hope that theoretical insight into the cause of evolutionary stagnation in current methods will help to enable the automation of robot design and behavioural training-while simultaneously providing a test bed to investigate the theory of embodied cognition.

关键词： embodied cognition evolutionary robotics brain-body co-optimization morphological optimization soft robotics

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