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Genetic and evolutionary Computation Conference (GECCO)

作者： Medvet, Eric Bartoli, Alberto De Lorenzo, Andrea Fidel, Giulio Univ Trieste DIA Trieste Italy

ISBN: (纸本)9781450371285

Voxel-based soft robots (VSRs) are aggregations of elastic, cubic blocks that have sparkled the interest of robotics and Artificial Life researchers. VSRs can move by varying the volume of individual blocks, according to control signals dictated by a controller, possibly based on inputs coming from sensors embedded in the blocks. Neural networks (NNs) have been used as centralized processing units for those sensing controllers, with weights optimized using evolutionary computation. This structuring breaks the intrinsic modularity of VSRs: decomposing a VSR into modules to be assembled in a different way is very hard. In this work we propose an alternative approach that enables full modularity and is based on a distributed neural controller. Each block contains a small NN that outputs signals to adjacent blocks and controls the local volume, based on signals from adjacent blocks and on local sensor readings. We show experimentally for the locomotion task that our controller is as effective as the centralized one. Our experiments also suggest that the proposed framework indeed allows exploiting modularity: VSRs composed of pre-trained parts (body and controller) can be evolved more efficiently than starting from scratch.

关键词： evolutionary robotics Modularity Neuroevolution

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Coordination and synchronization of locomotion in a virtual robot 9

Coordination and synchronization of locomotion in a virtual ...

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9th International Conference on Neural Information Processing

作者： Teo, J Abbass, HA Univ New S Wales Sch Comp Sci ALAR Lab Canberra ACT Australia

ISBN: (纸本)9810475241

This paper investigates the use of a multi-objective approach for evolving artificial neural networks that act as controllers for the legged locomotion of a 3-dimensional, artificial quadruped creature simulated in a physics-based environment. The Pareto-frontier Differential Evolution (PDE) algorithm is used to generate a pareto optimal set of artificial neural networks that optimizes the conflicting objectives of maximizing locomotion behavior and minimizing neural network complexity. Here we provide an insight into how the controller generates the emergent. walking behavior in the creature by analyzing the evolved artificial neural networks in operation. A comparison between pareto optimal controllers showed that ANNs with varying numbers of hidden units resulted in noticeably different locomotion behaviors. We also found that a much higher level of sensory-motor coordination was present in the best evolved controller.

关键词： artificial evolution artificial life embodied cognitive science evolutionary robotics

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A Hox Gene Inspired Generative Approach to Evolving Robot Morphology 13

A Hox Gene Inspired Generative Approach to Evolving Robot Mo...

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15th Genetic and evolutionary Computation Conference (GECCO)

作者： Samuelsen, Eivind Glette, Kyrre Torresen, Jim Univ Oslo Inst Informat N-0316 Oslo Norway

ISBN: (纸本)9781450319638

This paperproposes anapproach to representing robot morphology and control, using a two-level description linked to two different physical axes of development. The bioinspired encoding produces robots with animal-like bilateral limbed morphology with co-evolved control parameters using a central pattern generator-based modular artificial neural network. Experiments are performed on optimizing a simple simulated locomotion problem, using multi-objective evolution with two secondary objectives. The results show that the representation is capable of producing a variety of viable designs even with a relatively restricted set of parameters and a very simple control system. Furthermore, the utility of a cumulative encoding over a non-cumulative approach is demonstrated. We also show that the representation is viable for real-life reproduction by automatically generating CAD files, 3D printing the limbs, and attaching off-the-shelf servomotors.

关键词： evolutionary robotics morphological evolution embryogenic encoding

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Evolving Polydisperse Soft Robotic Jamming Grippers 22

Evolving Polydisperse Soft Robotic Jamming Grippers

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Genetic and evolutionary Computation Conference (GECCO)

作者： Fitzgerald, Seth G. Delaney, Gary W. Howard, David Maire, Frederic QUT Data61 Robot & Autonomous Syst Grp Engn FacEER SchCSIRO Brisbane Qld Australia CSIRO Data61 Melbourne Vic Australia CSIRO Data61 Robot & Autonomous Syst Grp Brisbane Qld Australia QUT Engn Fac EER Sch Brisbane Qld Australia

ISBN: (纸本)9781450392686

Granular systems react to changes in external pressure by adapting their density through complex grain contact interactions. Granular packings subjected to small pressures are loose and fluidic, but are jammed into compressed, rigid packings at higher pressures. Common soft robotic jamming grippers are composed of a vacuum pump connected to a flexible membrane filled with granular material, e.g. ground coffee. The membrane encompasses an object, and the pump activates. The grains jam, deforming the membrane, and grasping the object, with grain morphology playing a critical role in determining gripper performance. Bespoke grippers can be designed to effectively grasp specific objects by evolving their constituent grains. Evolved grippers have to-date used exclusively monodisperse granular materials (grains with identical size and shape). However, while not conceived through evolution, polydisperse grippers comprised of natural grains varying in size and morphology can often perform better in real-world grasping experiments. We employ the Discrete Element Method and NSGA-III to optimise grasps on disparate objects by evolving distributions of superellipsoidal grains (varying both their shapes and volumes) within the gripper. Results elucidate the successful application of multi-objective evolution to design bespoke polydisperse jamming grippers, and how variations in grain surface curvatures and volumes influences grasping performance.

关键词： evolutionary robotics Granular Materials Soft robotics NSGA-III Multi-Objective Optimization Discrete Element Method Computational Modelling

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A Comparison of Multiobjective Algorithms in Evolving Quadrupedal Gaits 1

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14th International Conference on the Simulation of Adaptive Behavior (SAB)

作者： Moore, Jared M. McKinley, Philip K. Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

ISBN: (数字)9783319434889

ISBN: (纸本)9783319434889;9783319434872

Robotic systems, whether physical or virtual, must balance multiple objectives to operate effectively. Beyond performance metrics such as speed and turning radius, efficiency of movement, stability, and other objectives contribute to the overall functionality of a system. Optimizing multiple objectives requires algorithms that explore and balance improvements in each. In this paper, we evaluate and compare two multiobjective algorithms, NSGA-II and the recently proposed Lexicase selection, investigating distance traveled, efficiency, and vertical torso movement for evolving gaits in quadrupedal animats. We explore several variations of Lexicase selection, including different parameter configurations and weighting strategies. A control treatment evolving solely on distance traveled is also presented as a baseline. All three algorithms (NSGA-II, Lexicase, and Control) produce effective locomotion in the quadrupedal animat, but differences arise in performance and efficiency of movement. The NSGA-II algorithm significantly outperforms Lexicase selection in all three objectives, while Lexicase selection significantly outperforms the control in two of the three objectives.

关键词： evolutionary robotics Multiobjective algorithms Genetic algorithms Computational evolution Lexicase selection NSGA-II

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EVOLVING ROBUST ROBOT CONTROLLERS FOR CORRIDOR FOLLOWING USING GENETIC PROGRAMMING

EVOLVING ROBUST ROBOT CONTROLLERS FOR CORRIDOR FOLLOWING USI...

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2nd International Conference on Agents and Artificial Intelligence (ICAART 2010)

作者： Wyns, Bart Bonte, Bert Boullart, Luc Univ Ghent Dept Elect Energy Syst & Automat Technol Pk 913 Zwijnaarde Belgium

ISBN: (纸本)9789896740214

Designing robots and robot controllers is a highly complex and often expensive task. However, genetic programming provides an automated design strategy to evolve complex controllers based on evolution in nature. We show that, even with limited computational resources, genetic programming is able to evolve efficient robot controllers for corridor following in a simulation environment. Therefore, a mixed and gradual form of layered learning is used, resulting in very robust and efficient controllers. Furthermore, the controller is successfully applied to real environments as well.

关键词： Genetic programming evolutionary robotics Corridor following EyeBot

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Robot task switching in complex environments

Robot task switching in complex environments

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IEEE/ASME International Conference on Advanced Intelligent Mechatronics

作者： Capi, Genci Toyama Univ Fac Engn Toyama 930 Japan

ISBN: (纸本)9781424412631

Robots operating in everyday life environments are often required to switch between different tasks. This paper introduces a new method for multiple tasks performance based on multiobjective evolutionary algorithm, where each task is considered as a separate objective function. In order to verify the effectiveness, the proposed method is applied to evolve neural controllers for the Cyber Rodent robot that has to switch properly between three different tasks: (1) protecting another moving robot by following it closely (2) collecting objects scattered in the environment and (3) exploring the environment by moving close to the walls and obstacles. The simulation and experimental results using Cyber Rodent robot show that multiobjective-based evolutionary method can be applied effectively for generating neural networks controlling the robot to perform multiple tasks, simultaneously.

关键词： evolutionary robotics multiobjective evolution neural controller task complexity

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Robustness in Diversity: Quality-Diversity Driven Discovery of Agile Soft Robotic Gaits 7

Robustness in Diversity: Quality-Diversity Driven Discovery ...

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7th International Conference on Soft robotics (ROBOSOFT)

作者： Daly, John Casper, Daniel Farooq, Muhammad James, Andrew Khan, Ali Mulgrew, Phoenix Tyebkhan, Daniel Vo, Bao Rieffel, John Union Coll Schenectady NY 12308 USA Union Coll Dept Comp Sci Schenectady NY 12308 USA

ISBN: (纸本)9798350381818

Soft robotics aims to develop robots able to adapt their behavior across a wide range of unstructured and unknown environments. A critical challenge of soft robotic control is that nonlinear dynamics often result in complex behaviors that are hard to model and predict. Typically behaviors for mobile soft robots are discovered through empirical trial and error and hand-tuning. More recently, optimization algorithms such as Genetic Algorithms (GA) have been used to discover gaits, but these behaviors are often optimized for a single environment or terrain, and can be brittle to unplanned changes to terrain. In this paper we demonstrate how Quality Diversity Algorithms, which search of a range of high-performing behaviors, can produce repertoires of gaits that are robust to changing terrains. This robustness significantly out-performs that of gaits produced by a single objective optimization algorithm.

关键词： Soft robotics Quality Diversity Algorithms evolutionary robotics

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Neuronal plasticity and temporal adaptivity: GasNet robot control networks

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ADAPTIVE BEHAVIOR 2002年第3-4期10卷 161-183页

作者： Smith, T Husbands, P Philippides, A O'Shea, M Univ Sussex BIOLS CCNR Brighton BN1 9QH E Sussex England Univ Sussex COGS Sch Cognit & Comp Sci Brighton BN1 9QH E Sussex England Univ Sussex BIOLS Sch Biol Sci Brighton BN1 9QH E Sussex England

Designing controllers for autonomous robots is not an exact science, and there are few guiding principles on what properties of control systems are useful for what kinds of task. In this article we analyze the functional operation of robot controllers developed using evolutionary computation methods, to elucidate the strengths and weaknesses of the underlying control system class. By comparing and contrasting robot controllers based on two different classes of artificial neural network, the GasNet and NoGas networks, we show that the increased evolvability of the GasNet class on a visual shape discrimination task is due to the temporally adaptive nature of the GasNet, where neuronal plasticity mediated through the concentration of virtual neuromodulatory "gases" occurs over a wide range of time courses. We argue that the availability of mechanisms operating over a wide range of potential time courses is a crucial property for controllers used to generate adaptive behavior over time, and that the design process should easily be able to adapt those time courses to the natural time scales in the environment.

关键词： evolutionary robotics artificial neural networks GasNets neuromodulation neuronal plasticity

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Effects of environmental conditions on evolved robot morphologies and behavior

Effects of environmental conditions on evolved robot morphol...

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Genetic and evolutionary Computation Conference (GECCO)

作者： Miras, Karine Eiben, A. E. Vrije Univ Amsterdam Amsterdam Netherlands

ISBN: (纸本)9781450361118

This paper studies the effects of different environments on morphological and behavioral properties of evolving populations of modular robots. To assess these properties, a set of morphological and behavioral descriptors was defined and the evolving population mapped in this multi-dimensional space. Surprisingly, the results show that seemingly distinct environments can lead to the same regions of this space, i.e., evolution can produce the same kind of morphologies/behaviors under conditions that humans perceive as quite different. These experiments indicate that demonstrating the `ground truth' of evolution stating the firm impact of the environment on evolved morphologies is harder in evolutionary robotics than usually assumed.

关键词： evolutionary robotics evolvable morphologies generative encoding environment

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