UCT CS Research Document Archive

Evolving Morphological Robustness for Collective Robotics

Putter, Ruben and Geoff Nitschke (2017) Evolving Morphological Robustness for Collective Robotics. In Proceedings IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), Honolulu, USA.

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This study evaluates a Neuro-Evolution (NE) method for controller evolution in simulated robot teams, where the goal is to evaluate the morphological robustness of evolved controllers. Artificial Neural Network (ANN) controllers are evolved for a specific sensory configuration (morphology) and then evaluated on a set of different morphologies. The morphological robustness of evolved controllers is evaluated according to team task performance given a collective construction task of increasing complexity. The overall objective was to ascertain an appropriate method for evolving ANN controllers that are readily transferable to robot teams with varied morphologies. Such controller transfer is necessary if task specifications change and different sensory configurations are required, or if robots are damaged and some sensors become disabled. In both cases it is ideal if teams continue to exhibit consistent behavior and a similar task performance. Results indicate that an indirect (developmental) encoding NE method consistently evolves controllers that fully function when transferred to teams with varied morphologies. That is, where comparable or higher task performances were yielded compared to controllers evolved specifically for the varied morphology.

EPrint Type:Conference Paper
Subjects:I Computing Methodologies: I.2 ARTIFICIAL INTELLIGENCE
ID Code:1180
Deposited By:Nitschke, Geoff
Deposited On:23 November 2017