Three-fold Adaptivity in Groups of Robots

Abstract

Adapting the control systems of robots on the fly is important in robotic systems of the future. In this paper we present and investigate a three-fold adaptive system based on evolution, individual and social learning in a group of robots and report on a proof-of-concept study based on epucks. We distinguish inheritable and learnable components in the robots' makeup, specify and implement operators for evolution, learning and social learning, and test the system in an arena where the task is to learn to avoid obstacles. In particular, we make the sensory layout evolvable, the locomotion control system learnable and investigate the effects of including social learning in the 'adaptation engine'. Our simulation experiments demonstrate that the full mix of three adaptive mechanisms is practicable and that adding social learning leads to better controllers faster.

Evolution, Individual Learning, and Social Learning in a Swarm of Real Robots

“…These plots clearly show that social learning improves performance even if the sensory layouts, hence the number of input nodes in the NN controllers, vary over the members of the population. This result is consistent with the ones obtained in simulations using swarms of e-puck robots [9].…”

“…The key innovation behind the system we investigate here is the 'adaptation engine' that integrates evolution, individual learning, and social learning [8], [9]. The distinction between evolution on the one hand and lifetime learning on the other hand is based on distinguishing two types of adaptable robot features: inheritable features (genome) and learnable features (memome).…”

Importance of Parameter Settings on the Benefits of Robot-to-Robot Learning in Evolutionary Robotics

“…There is ample evidence that set-ups, where robots can share knowledge, outperform otherwise equivalent set-ups where robots learn in isolation.When robots share knowledge, they achieve better performance and/or the learning curve is steeper (Usui and Arita, 2003 ; Curran and ORiordan, 2007 ; Perez et al, 2008 ; Pugh and Martinoli, 2009 ; Garca-Sanchez et al, 2012 ; Miikkulainen et al, 2012 ; Tansey et al, 2012 ; Heinerman et al, 2015a , b ; Jolley et al, 2016 ). A higher overall performance can be observed when there is a quality or diversity assessment before the knowledge is sent or incorporated (Huijsman et al, 2011 ; Garca-Sanchez et al, 2012 ; Heinerman et al, 2015b ).…”

Distributed On-line Learning in Swarm Robotics with Limited Communication Bandwidth

“…Both embodied evolution [4], [21] and social learning [12] implement an evolutionary algorithm scheme, adapted to perform distributed on-line learning as illustrated in Figure 1. Each robot optimises a policy to maximise a score, G, that is locally defined in each robot.…”