The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics

Abstract : The reality gap, that often makes controllers evolved in simulation inefficient once transferred onto the physical robot, remains a critical issue in Evolutionary Robotics (ER). We hypothesize that this gap highlights a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: the most efficient solutions in simulation often exploit badly modeled phenomena to achieve high fitness values with unrealistic behaviors. This hypothesis leads to the Transferability approach, a multi-objective formulation of ER in which two main objectives are optimized via a Pareto-based Multi-Objective Evolutionary Algorithm: (1) the fitness and (2) the transferability, estimated by a simulation-to-reality (STR) disparity measure. To evaluate this second objective, a surrogate model of the exact STR disparity is built during the optimization. This Transferability approach has been compared to two reality-based optimization methods, a noise-based approach inspired from Jakobis minimal simulation methodology and a local search approach. It has been validated on two robotic applications: 1) a navigation task with an e-puck robot; 2) a walking task with an 8-DOF quadrupedal robot. For both experimental set-ups, our approach successfully finds efficient and well-transferable controllers only with about ten experiments on the physical robot.
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Contributor : Jean-Baptiste Mouret <>
Submitted on : Sunday, April 15, 2012 - 2:45:06 PM
Last modification on : Friday, May 24, 2019 - 5:23:42 PM
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Sylvain Koos, Jean-Baptiste Mouret, Stéphane Doncieux. The Transferability Approach: Crossing the Reality Gap in Evolutionary Robotics. IEEE Transactions on Evolutionary Computation, Institute of Electrical and Electronics Engineers, 2012, pp.1-25. ⟨10.1109/TEVC.2012.2185849⟩. ⟨hal-00687617⟩



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