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Objective versus Non-Objective Search in Evolving Morphologically Robust Robot Controllers

机译:演化形态上鲁棒的机器人控制器中的目标搜索与非目标搜索

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This study evaluates objective versus non-objective-based evolutionary search methods for behavior evolution in robot teams. The goal is to evaluate the morphological robustness of evolved controllers, where controllers are evolved for specific robot sensory-motor configurations (morphologies) but must continue to function as these morphologies degrade. Robots use artificial neural network controllers where behavior evolution is directed by developmental neuro-evolution. Guiding evolutionary controller design we use objective (fitness function) versus non-objective (novelty) search. The former optimizes for behavioral fitness and the latter for behavioral novelty. These methods are evaluated across varying robot morphologies and increasing task complexity. Results indicate that novelty search yields no benefits over objective search, in terms of evolving morphologically robust controllers. That is, both novelty and objective search evolve team controllers that are morphologically robust given varying robot morphologies and increasing task complexity. Results thus suggest behavioral diversity methods such as novelty search mat not be suitable for generating robot behaviors that can continue functioning given changing robot morphologies, for example, due to damaged or disabled sensors and actuators.
机译:这项研究评估了基于客观与非客观的进化搜索方法,以进行机器人团队中行为的演化。目的是评估演化控制器的形态鲁棒性,其中控制器针对特定的机器人感觉运动配置(形态)而演化,但是随着这些形态的退化,控制器必须继续起作用。机器人使用人工神经网络控制器,其中行为的发展由发育的神经进化指导。指导进化控制器设计,我们使用目标(适应度函数)与非目标(新颖性)搜索。前者针对行为适应性进行优化,而后者针对行为新颖性进行优化。这些方法在各种机器人形态和不断增加的任务复杂性中得到了评估。结果表明,就形态上鲁棒的控制器而言,新颖性搜索没有比客观搜索产生任何好处。也就是说,新颖性和客观搜索都会发展团队控制器,在给定各种机器人形态并增加任务复杂性的情况下,它们在形态上都非常强大。因此,结果表明,行为多样性方法(如新颖的搜索垫)不适合于生成机器人行为,这些行为在给定不断变化的机器人形态的情况下可以继续发挥作用,例如由于传感器和执行器损坏或失效。

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