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From Animals to Animats 14

Evolutionary robotics uses evolutionary algorithms to automatically design both body and controller of a robot. We describe two contributions to automated design of virtual robotic creatures. First, we introduce a nature-inspired method that allows virtual robots to modify their morphology through lifetime learning. We show that such morphological plasticity makes it possible to evolve robots that can dynamically adjust their morphology to the environment they are placed into. We also show that by reshaping the fitness landscape, learning reduces computation cost required to evolve a robot with a given target fitness even in a single environment. In the second contribution, we show that for certain problems in evolutionary robotics, premature convergence to local optima can be avoided by ignoring the original objective and searching for any novel behaviors instead (a technique known as Novelty Search).

Evolution and Learning of Virtual Robots

Many animals are able to modify their morphology during their lifetime in response to changes in the environment. Such modifications are often adaptive—they can improve individual’s chances of survival and reproduction. In this paper we explore the effects of such morphological plasticity on body–brain coevolution of virtual creatures. We propose a method where morphological plasticity is achieved through learning during individual’s lifetime allowing each individual to quickly adapt its morphology to the current environment. We show that the resulting plasticity allows evolution of creatures better adapted to different simulated environments. We also show that evolution combined with the new learning rule reduces the total computational cost required to evolve an individual with a given target fitness compared to evolution without learning.

Effects of morphological plasticity on evolution of virtual robots

Many animals are able to modify their morphology during their lifetime in response to changes in the environment. Such modifications are often adaptive—they can improve individual’s chances of survival and reproduction. In this paper we explore the effects of such morphological plasticity on body-brain coevolution of virtual creatures. We propose a method where morphological plasticity is achieved through learning during individual’s lifetime allowing each individual to quickly adapt its morphology to the current environment. We show that the resulting plasticity allows evolution of creatures better adapted to different simulated environments. We also show that evolution combined with the new learning rule reduces the total computational cost required to evolve an individual with a given target fitness compared to evolution without learning.

Adaptation of Virtual Creatures to Different Environments Through Morphological Plasticity

The Voxbot is a cubic (voxel) shaped robot actuated by expansion and contraction of its 12 edges designed for running evolutionary experiments, built as cheaply as possible Each edge was made of a single 10ml medical syringe for pneumatic control These were connected to an array of 12 servos situated on an external housing and controlled with an Arduino microcontroller from a laptop With twenty motor primitive commands and the slow response of its pneumatics this robot allows real time controllers to be evolved in situ rather than just in simulation With simple combinations and sequencing of motor primitives the Voxbot can be made to walk, rotate and crab crawl The device is available in kit form and is very easy to build and replicate Other morphologies can be built easily

Nikolaos Katsaros

Papers

Voxel Robot: A Pneumatic Robot with Deformable Morphology