An Evolutionary Approach to Damage Recovery of Robot Motion with Muscles

Mahdavi, Siavash Haroun; Bentley, Peter J.

doi:10.1007/978-3-540-39432-7_27

Cited by 19 publications

(15 citation statements)

References 6 publications

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“…This has previously been demonstrated on modular robots engaged in locomotion and self-reconfiguration [1], [2], [7], [24], [28]. For example, in a paper by Mahadavi and Bentley [16] the control of a snake like robot was optimized online using a genetic algorithm. The algorithm was shown to recover from failures in the SMAs actuating the robot.…”

Section: Related Workmentioning

confidence: 99%

Adaptive strategy for online gait learning evaluated on the polymorphic robotic LocoKit

Christensen

Larsen

Støy

2012

2012 IEEE Conference on Evolving and Adaptive Intelligent Systems

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Abstract-This paper presents experiments with a morphologyindependent, life-long strategy for online learning of locomotion gaits, performed on a quadruped robot constructed from the LocoKit modular robot. The learning strategy applies a stochastic optimization algorithm to optimize eight open parameters of a central pattern generator based gait implementation. We observe that the strategy converges in roughly ten minutes to gaits of similar or higher velocity than a manually designed gait and that the strategy readapts in the event of failed actuators. In future work we plan to study co-learning of morphological and control parameters directly on the physical robot.

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Section: Related Workmentioning

confidence: 99%

Adaptive strategy for online gait learning evaluated on the polymorphic robotic LocoKit

Christensen

Larsen

Støy

2012

2012 IEEE Conference on Evolving and Adaptive Intelligent Systems

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“…There are several approaches to the challenge of controller transferal, including adding noise to the simulated robot's sensors [32]; adding generic safety margins to the simulated objects comprising the physical system [26]; evolving directly on the physical system ( [23], [47] and [59]); evolving first in simulation followed by further adaptation on the physical robot ( [47], [51]); or implementing some neural plasticity that allows the physical robot to adapt during its lifetime to novel environments ( [19], [24], [60]). …”

Section: Application 3: Evolutionary Roboticsmentioning

confidence: 99%

Nonlinear System Identification Using Coevolution of Models and Tests

Bongard

Lipson

2005

IEEE Trans. Evol. Computat.

112

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We present a coevolutionary algorithm for inferring the topology and parameters of a wide range of hidden nonlinear systems with a minimum of experimentation on the target system. The algorithm synthesizes an explicit model directly from the observed data produced by intelligently generated tests. The algorithm is composed of two coevolving populations. One population evolves candidate models that estimate the structure of the hidden system. The second population evolves informative tests that either extract new information from the hidden system or elicit desirable behavior from it. The fitness of candidate models is their ability to explain behavior of the target system observed in response to all tests carried out so far; the fitness of candidate tests is their ability to make the models disagree in their predictions. We demonstrate the generality of this estimation-exploration algorithm by applying it to four different problems-grammar induction, gene network inference, evolutionary robotics, and robot damage recovery-and discuss how it overcomes several of the pathologies commonly found in other coevolutionary algorithms. We show that the algorithm is able to successfully infer and/or manipulate highly nonlinear hidden systems using very few tests, and that the benefit of this approach increases as the hidden systems possess more degrees of freedom, or become more biased or unobservable. The algorithm provides a systematic method for posing synthesis or analysis tasks to a coevolutionary system.

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“…To address this limitation, evolutionary optimization of gaits can be performed directly on the physical robot [64][65][66]. However, sequential evaluation of a population of gaits is not fitting for lifelong learning since it requires a steady convergence to a single gait.…”

Section: Adaptive Self-reconfigurable Modular Robotsmentioning

confidence: 99%