Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics

TL;DR

This paper introduces a systematic method for deriving behaviour characterisations in evolutionary robotics, enabling more effective and task-relevant diversity measures than ad hoc or generic approaches.

Contribution

It presents a novel approach for systematically deriving behaviour characterisations based on formal descriptions of agents and environments, improving over prior ad hoc or generic methods.

Findings

SDBCs perform comparably to task-specific characterisations in solution quality.

SDBCs enhance behaviour space exploration in collective robotics tasks.

The approach integrates task-specific features, internal states, and environmental relations.

Abstract

Evolutionary techniques driven by behavioural diversity, such as novelty search, have shown significant potential in evolutionary robotics. These techniques rely on priorly specified behaviour characterisations to estimate the similarity between individuals. Characterisations are typically defined in an ad hoc manner based on the experimenter's intuition and knowledge about the task. Alternatively, generic characterisations based on the sensor-effector values of the agents are used. In this paper, we propose a novel approach that allows for systematic derivation of behaviour characterisations for evolutionary robotics, based on a formal description of the agents and their environment. Systematically derived behaviour characterisations (SDBCs) go beyond generic characterisations in that they can contain task-specific features related to the internal state of the agents, environmental features, and relations between them. We evaluate SDBCs with novelty search in three simulated collective robotics tasks. Our results show that SDBCs yield a performance comparable to the task-specific characterisations, in terms of both solution quality and behaviour space exploration.