Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

Abstract

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

Robot swarms as an educational tool: The Thymio’s way

“…37,38 On the other hand, we introduce a strictly collaborative task inspired by the classic 'stick-pulling experiment'. 39,40 Finally, we draw some conclusions in the light of pilot demonstrations performed with kids at the International Robotics Festival held in Pisa in September 2017, 41 which corroborate our proposal with initial indications about the acceptance of Thymio robot swarms as an educational tool.…”

AutoMoDe-Chocolate: automatic design of control software for robot swarms

“…Multi-objectivization has been proposed as a general way to guide the evolutionary search in rugged fitness landscapes and avoid bootstrap problems, both problems severely affecting the evolution of control software for robot swarms (Trianni and López-Ibáñez 2014). However, no test with robots has been performed to date, and multi-objective evolution could be affected by the reality gap problem as much as single-objective evolution.…”

Evolutionary Robotics: Model or Design?

“…This would better delineate the features of ER as a design tool, and would produce guidelines, which benefit developers who wish to exploit ER for their applications. Novel algorithmic solutions and design methods should also be proposed, and systematically contrasted with the state of the art, possibly in the context of a wellconceived benchmarking exercise (Clune et al, 2011;Mouret and Doncieux, 2012;Trianni and López-Ibáñez, 2014). Benchmarking would also be useful with respect to other control approaches, in order to identify the benefits and drawbacks of ER against the methodologies developed in other domains.…”

“…Improvement on conventional methods can be achieved by techniques that enhance the ability to search the space of all potential solutions. Trianni and López-Ibáñez (2014) discuss the usage of multi-objective optimization in ER, and identify the related advantages. Lehman and Stanley (2011) propose "novelty search" as a methodology of avoiding deception from ill-defined fitness functions, and to explore the solution space more widely.…”