Evolution can be employed for two goals. Firstly, to provide a force for adaptation to the environment as it does in nature and in many artificial life implementations -this allows the evolving population to survive. Secondly, evolution can provide a force for optimisation as is mostly seen in evolutionary robotics research -this causes the robots to do something useful. We propose the monee algorithmic framework as an approach to combine these two facets of evolution: to combine environment-driven and task-driven evolution. To achieve this, monee employs environmentdriven and task-based parent selection schemes in parallel.We test this approach in a simulated experimental setting where the robots are tasked to collect two different kinds of puck. Monee allows the robots to adapt their behaviour to successfully tackle these tasks while ensuring an equitable task distribution at no cost in task performance through a market-based mechanism. In environments that discourage robots performing multiple tasks and in environments where one task is easier than the other, monee's market mechanism prevents the population completely focussing on one task.
This paper addresses a principal problem of in vivo evolution of modular multi-cellular robots. To evolve robot morphologies and controllers in real-space and real-time we need a generic learning mechanism that enables arbitrary modular shapes to obtain a suitable gait quickly after 'birth'. In this study we investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. The experiments give insights into the online dynamics of gait learning, the distribution of lucky / unlucky runs and their dependence on the size and complexity of the modular robotic organisms.
Abstract. This paper addresses a principal problem of in vivo evolution of modular multi-cellular robots. To evolve robot morphologies and controllers in real-space and real-time we need a generic learning mechanism that enables arbitrary modular shapes to obtain a suitable gait quickly after 'birth'. In this study we investigate a reinforcement learning method and conduct simulation experiments using robot morphologies with different size and complexity. The experiments give insights into the online dynamics of gait learning, the distribution of lucky / unlucky runs and their dependence on the size and complexity of the modular robotic organisms.
This paper is inspired by a vision of self-sufficient robot collectives that adapt autonomously to deal with their environment and to perform user-defined tasks at the same time. We introduce the monee algorithm as a method of combining open-ended (to deal with the environment) and task-driven (to satisfy user demands) adaptation of robot controllers through evolution. A number of experiments with simulated e-pucks serve as proof of concept and show that with monee, the robots adapt to cope with the environment and to perform multiple tasks. Our experiments indicate that monee distributes the tasks evenly over the robot collective without undue emphasis on easy tasks.
This paper is concerned with on-line evolutionary robotics, where robot controllers are being evolved during a robots' operative time. This approach offers the ability to cope with environmental changes without human intervention, but to be effective it needs an automatic parameter control mechanism to adjust the evolutionary algorithm (EA) appropriately. In particular, mutation step sizes (σ) and the time spent on fitness evaluation (τ ) have a strong influence on the performance of an EA. In this paper, we introduce and experimentally validate a novel method for self-adapting τ during runtime. The results show that this mechanism is viable: the EA using this self-adaptative control scheme consistently shows decent performance without a priori tuning or human intervention during a run.
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