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Special Issue of the IEEE Transactions on Cognitive and Developmental Systems

Special Issue on Towards autonomous evolution, (re)production and learning in robotic eco-systems

Important Dates

15 March 2021 Deadline for manuscript submission
2 April 2021 Notification of authors
2 July 2021 Deadline for revised manuscripts
August 2021 Final version

Aim and Scope

This special issue will include state-of-the-art research on autonomous evolution, reproduction and learning in robotic eco-systems.

Advances in 3D printing technologies now enable us to print robot bodies from novel materials that flex and electronic circuitry using conducting inks. At the same time, new insights into evolutionary learning and adaptation promise new methods of developing adaptive control. Coupling these together, it is possible to perceive a long-term vision of a new technology enabling the evolution of entire autonomous robotic ecosystems that live and work for long periods in challenging and dynamic environments without the need for direct human oversight. In such a radically new autonomous system, robots will be conceived and born, rather than designed and manufactured. This will fundamentally change the concept of machines. Development is driven by evolution that can discover novel morphologies and controllers that are highly adapted to operate in these environments, which could not have been anticipated by human designer, and can be further enhanced via learning mechanisms that refine an individual’s behavior over its lifetime. The result is a new breed of machines that can change their form and behaviour, not in error but on purpose.

This Special Issue solicits novel research that will contribute towards achieving such a vision. Although evolving in hardware directly addresses the reality-gap, it is clear that this can be complemented and/or accelerated by simulation; significant progress will likely require combining evolution in both hardware and simulation.


The primary list of topics of interest includes, but not limited to:

  • Novel methods for simultaneous evolution of morphology and control
  • Novel methods for facilitating learning and adaptation during lifetime
  • Evolution of learnability in a robot population
  • Investigating the balance between morphological intelligence and brain intelligence
  • Robot evolution in hardware
  • Evolution of morphologies using novel materials
  • Simulation of soft robots
  • Closing the reality gap
  • Evolving behavioural/morphological diversity within a robotic eco-system
  • Issues related to manufacturability and viability of robotic genotypes
  • Surrogate methods for fitness evaluations


Manuscripts should be prepared according to the “Information for Authors” of the journal found at and submissions should be done through the IEEE TCDS Manuscript center: and please select the category “SI: AEPL”.

Guest Editor

For further information, please contact one of the following Guest Editors:

Professor Andy Tyrrell
University of York, UK

Professor Emma Hart
Edinburgh Napier University, Scotland

Professor Gusz Eiben
Vrije Universiteit Amsterdam, The Netherlands