Crossing the Reality Gap in Evolutionary Robotics by Promoting Transferable Controllers

Abstract : The reality gap, that often makes controllers evolved in simulation inefficient once transferred onto the real system, remains a critical issue in Evolutionary Robotics (ER); it prevents ER application to real-world problems. We hypothesize that this gap mainly stems from a conflict between the efficiency of the solutions in simulation and their transferability from simulation to reality: best solutions in simulation often rely on bad simulated phenomena (e.g. the most dynamic ones). This hypothesis leads to 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. To evaluate this second objective, a simulation-to-reality disparity value is approximated for each controller. The proposed method is applied to the evolution of walking controllers for a real 8-DOF quadrupedal robot. It successfully finds effi- cient and well-transferable controllers with only a few experiments in reality.
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Sylvain Koos, Jean-Baptiste Mouret, Stéphane Doncieux. Crossing the Reality Gap in Evolutionary Robotics by Promoting Transferable Controllers. Conference on Genetic and Evolutionary Computation, Jul 2010, United States. pp.119-126. ⟨hal-00633927⟩

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