An Approach to Anytime Learning
ML '92 Proceedings of the Ninth International Workshop on Machine Learning
Solution concepts in coevolutionary algorithms
Solution concepts in coevolutionary algorithms
Learning to kick the ball using back to reality
RoboCup 2004
Self-modeling in humanoid soccer robots
Robotics and Autonomous Systems
Proceedings of the 2009 Annual Research Conference of the South African Institute of Computer Scientists and Information Technologists
Crossing the reality gap in evolutionary robotics by promoting transferable controllers
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A review of gait optimization based on evolutionary computation
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
A comparison of sampling strategies for parameter estimation of a robot simulator
SIMPAR'12 Proceedings of the Third international conference on Simulation, Modeling, and Programming for Autonomous Robots
Generating diverse behaviors of evolutionary robots with speciation for theory of mind
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
Linear reactive control for efficient 2D and 3D bipedal walking over rough terrain
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Architecture of a cyberphysical avatar
Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems
Encouraging reactivity to create robust machines
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
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Evolutionary Robotics (ER) is a promising methodology, intended for the autonomous development of robots, in which their behaviors are obtained as a consequence of the structural coupling between robot and environment. It is essential that there be a great amount of interaction to generate complex behaviors. Thus, nowadays, it is common to use simulation to speed up the learning process; however simulations are achieved from arbitrary off-line designs, rather than from the result of embodied cognitive processes. According to the reality gap problem, controllers evolved in simulation usually do not allow the same behavior to arise once transferred to the real robot. Some preliminary approaches for combining simulation and reality exist in the ER literature; nonetheless, there is no satisfactory solution available. In this work we discuss recent advances in neuroscience as a motivation for the use of environmentally adapted simulations, which can be achieved through the co-evolution of robot behavior and simulator. We present an algorithm in which only the differences between the behavior fitness obtained in reality versus that obtained in simulations are used as feedback for adapting a simulation. The proposed algorithm is experimentally validated by showing the successful development and continuous transference to reality of two complex low-level behaviors with Sony AIBO1 robots: gait optimization and ball-kicking behavior.