Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks

Biological plastic neural networks are systems of extraordinary computational capabilities shaped by evolution, development, and lifetime learning. The interplay of these elements leads to the emergence of adaptive behavior and intelligence. Inspired by such intricate natural phenomena, Evolved Plastic Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed plastic neural networks with a large variety of dynamics, architectures, and plasticity rules: these artificial systems are composed of inputs, outputs, and plastic components that change in response to experiences in an environment. These systems may autonomously discover novel adaptive algorithms, and lead to hypotheses on the emergence of biological adaptation. EPANNs have seen considerable progress over the last two decades. Current scientific and technological advances in artificial neural networks are now setting the conditions for radically new approaches and results. In particular, the limitations of hand-designed networks could be overcome by more flexible and innovative solutions. This paper brings together a variety of inspiring ideas that define the field of EPANNs. The main methods and results are reviewed. Finally, new opportunities and developments are presented

Embodied Evolution in Collective Robotics: A Review

This paper provides an overview of evolutionary robotics techniques applied to on-line distributed evolution for robot collectives -- namely, embodied evolution. It provides a definition of embodied evolution as well as a thorough description of the underlying concepts and mechanisms. The paper also presents a comprehensive summary of research published in the field since its inception (1999-2017), providing various perspectives to identify the major trends. In particular, we identify a shift from considering embodied evolution as a parallel search method within small robot collectives (fewer than 10 robots) to embodied evolution as an on-line distributed learning method for designing collective behaviours in swarm-like collectives. The paper concludes with a discussion of applications and open questions, providing a milestone for past and an inspiration for future research.Comment: 23 pages, 1 figure, 1 tabl

Toward energy Autonomy in heterogeneous Modular Plant-Inspired Robots through Artificial evolution

Contemporary robots perform energy intensive tasks—e.g., manipulation and locomotion—making the development of energy autonomous robots challenging. Since plants are primary energy producers in natural ecosystems, we took plants as a source of inspiration for designing our robotics platform. This led us to investigate energy autonomy in robots through employing solar panels. As plants move slowly compared to other large terrestrial organisms, it is expected that plant-inspired robots can enable robotic applications, such as long-term monitoring and exploration, where energy consumption could be minimized. Since it is difficult to manually design robotic systems that adhere to full energy autonomy, we utilize evolutionary algorithms to automate the design and evaluation of energy harvesting robots. We demonstrate how artificial evolution can lead to the design and control of a modular plant-like robot. Robotic phenotypes were acquired through implementing an evolutionary algorithm, a generative encoding and modular building blocks in a simulation environment. The generative encoding is based on a context sensitive Lindenmayer-System (L-System) and the evolutionary algorithm is used to optimize compositions of heterogeneous modular building blocks in the simulation environment. Phenotypes that evolved from the simulation environment are in turn transferred to a physical robot platform. The robotics platform consists of five different types of modules: (1) a base module, (2) a cube module, (3) servo modules, and (4,5) two types of solar panel modules that are used to harvest energy. The control system for the platform is initially evolved in the simulation environment and afterward transferred to an actual physical robot. A few experiments were done showing the relationship between energy cost and the amount of light tracking that evolved in the simulation. The reconfigurable modular robots are eventually used to harvest light with the possibility to be reconfigured based on the needs of the designer, the type of usable modules, and/or the optimal configuration derived from the simulation environment. Long-term energy autonomy has not been tested in this robotics platform. However, we think our robotics platform can serve as a stepping stone toward full energy autonomy in modular robots

Integration of Action and Language Knowledge: A Roadmap for Developmental Robotics

“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.”This position paper proposes that the study of embodied cognitive agents, such as humanoid robots, can advance our understanding of the cognitive development of complex sensorimotor, linguistic, and social learning skills. This in turn will benefit the design of cognitive robots capable of learning to handle and manipulate objects and tools autonomously, to cooperate and communicate with other robots and humans, and to adapt their abilities to changing internal, environmental, and social conditions. Four key areas of research challenges are discussed, specifically for the issues related to the understanding of: 1) how agents learn and represent compositional actions; 2) how agents learn and represent compositional lexica; 3) the dynamics of social interaction and learning; and 4) how compositional action and language representations are integrated to bootstrap the cognitive system. The review of specific issues and progress in these areas is then translated into a practical roadmap based on a series of milestones. These milestones provide a possible set of cognitive robotics goals and test scenarios, thus acting as a research roadmap for future work on cognitive developmental robotics.Peer reviewe

The Watchmaker's guide to Artificial Life: On the Role of Death, Modularity and Physicality in Evolutionary Robotics

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