Behavioral Specialization in Embodied Evolutionary Robotics: Why So Difficult?

Embodied evolutionary robotics is an on-line distributed learning method used in collective robotics where robots are facing open environments. This paper focuses on learning behavioral specialization, as defined by robots being able to demonstrate different kind of behaviors at the same time (e.g., division of labor). Using a foraging task with two resources available in limited quantities, we show that behavioral specialization is unlikely to evolve in the general case, unless very specific conditions are met regarding interactions between robots (a very sparse communication network is required) and the expected outcome of specialization (specialization into groups of similar sizes is easier to achieve). We also show that the population size (the larger the better) as well as the selection scheme used (favoring exploration over exploitation) both play important – though not always mandatory – roles. This research sheds light on why existing embodied evolution algorithms are limited with respect to learning efficient division of labor in the general case, i.e., where it is not possible to guess before deployment if behavioral specialization is required or not, and gives directions to overcome current limitations.This work is supported by the European Unions Horizon 2020 research and innovation programme under grant agreement No 640891, and the ERC Advanced Grant EPNet (340828). Part of the experiments presented in this paper were carried out using the Grid’5000 experimental testbed, being developed under the INRIA ALADDIN development action with support from CNRS, RENATER, and several Universities as well as other funding bodies (see https://www.grid5000.fr). The other parts of the simulations have been done in the supercomputer MareNostrum at Barcelona Supercomputing Center – Centro Nacional de Supercomputacion (The Spanish National Supercomputing Center).Peer ReviewedPostprint (published version

On the design of generalist strategies for swarms of simulated robots engaged in a task-allocation scenario

This study focuses on issues related to the evolutionary design of task-allocation mechanisms for swarm robotics systems with agents potentially capable of performing different tasks. Task allocation in swarm robotics refers to a process that results in the distribution of robots to different concurrent tasks without any central or hierarchical control. In this paper, we investigate a scenario with two concurrent tasks (i.e. foraging and nest patrolling) and two environments in which the task priorities vary. We are interested in generating successful groups made of behaviourally plastic agents (i.e. agents that are capable of carrying out different tasks in different environmental conditions), which could adapt their task preferences to those of their group mates as well as to the environmental conditions. We compare the results of three different evolutionary design approaches, which differ in terms of the agents’ genetic relatedness (i.e. groups of clones and groups of unrelated individuals), and/or the selection criteria used to create new populations (i.e. single and multi-objective evolutionary optimisation algorithms). We show results indicating that the evolutionary approach based on the use of genetically unrelated individuals in combination with a multi-objective evolutionary optimisation algorithm has a better success rate then an evolutionary approach based on the use of genetically related agents. Moreover, the multi-objective approach, when compared to a single-objective approach and genetically unrelated individual, significantly limits the tendency towards task specialisation by favouring the emergence of generalist agents without introducing extra computational costs. The significance of this result is discussed in view of the relationship between individual behavioural skills and swarm effectiveness