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

Nitschke, Geoff and Putter, Ruben (2018) Objective versus Non-Objective Search in Evolving Morphologically Robust Robot Controllers, Proceedings of 2018 Symposium Series on Computational Intelligence (IEEE SSCI 2018), Bengaluru, India.

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Abstract

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 nonobjective (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 may 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.

Item Type:	Conference paper
Subjects:	Computing methodologies > Artificial intelligence
Date Deposited:	09 Nov 2018
Last Modified:	10 Oct 2019 15:31
URI:	http://pubs.cs.uct.ac.za/id/eprint/1280

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