Abstract
Using passive compliance in robotic locomotion has been seen as a cheap and straightforward way of increasing the performance in energy consumption and robustness. However, the control for such systems remains quite challenging when using traditional robotic techniques. The progress in machine learning opens a horizon of new possibilities in this direction but the training methods are generally too long and laborious to be conducted on a real robot platform. On the other hand, learning a control policy in simulation also raises a lot of complication in the transfer. In this paper, we designed a cheap quadruped robot and detail a calibration method to optimize a simulation model in order to facilitate the transfer of parametric motor primitives. We present results validating the transfer of Central Pattern Generators (CPG) learned in simulation to the robot which already give positive insights on the validity of this method.
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Acknowledgments
This research has received funding from the European Unions Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 720270 (Human Brain Project SGA1).
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Urbain, G., Vandesompele, A., Wyffels, F., Dambre, J. (2018). Calibration Method to Improve Transfer from Simulation to Quadruped Robots. In: Manoonpong, P., Larsen, J., Xiong, X., Hallam, J., Triesch, J. (eds) From Animals to Animats 15. SAB 2018. Lecture Notes in Computer Science(), vol 10994. Springer, Cham. https://doi.org/10.1007/978-3-319-97628-0_9
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