The design of efficient locomotion gaits for robots with many degrees of freedom is challenging and time consuming even if optimization techniques are applied. Control parameters can be found through optimization in two ways: (i) through online optimization where the performance of a robot is measured while trying different control parameters on the actual hardware and (ii) through offline optimization by simulating the robot’s behavior with the help of models of the robot and its environment.
In this project, we explored a hybrid optimization method that combines the best properties of online and offline optimization to efficiently find locomotion gaits for arbitrary structures. In comparison to pure online optimization both the number of experiments using robotic hardware as well as the total time required for finding efficient locomotion gaits get highly reduced by running the major part of the optimization process in simulation using a cluster of processors. This becomes possible since we first optimized the parameters of the simulation environment so that the simulation results can be directly transfered to hardware.
The results of this project contributed to a publication at the IEEE/RSJ International Conference on Intelligent Robots and Systems:
R. Moeckel, Y. Perov, A. T. Nguyen, M. Vespignani, S. Bonardi, S. Pouya, A. Sproewitz, J. van den Kieboom, F. Wilhelm, A. J. Ijspeert, Hybrid gait optimization applied to Roombots modular robots, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2013